Startseite A novel autophagy-related long non-coding RNAs signature predicting progression-free interval and I-131 therapy benefits in papillary thyroid carcinoma
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A novel autophagy-related long non-coding RNAs signature predicting progression-free interval and I-131 therapy benefits in papillary thyroid carcinoma

  • Jie Hao , Shoujun Wang , Jinmiao Wang , Zhendong Zhang , Ming Gao EMAIL logo und Yajuan Wan EMAIL logo
Veröffentlicht/Copyright: 3. März 2023

Abstract

This study aimed to explore the prognostic and predictive value of autophagy-related lncRNAs in papillary thyroid carcinoma (PTC). The expression data of autophagy-related genes and lncRNAs of the PTC patients were obtained from TCGA database. Autophagy-related-differentially expressed lncRNAs (DElncs) were identified and used to establish the lncRNAs signature predicting patients’ progression-free interval (PFI) in the training cohort. Its performance was assessed in the training cohort, validation cohort, and entire cohort. Effects of the signature on I-131 therapy were also explored. We identified 199 autophagy-related-DElncs and constructed a novel six-lncRNAs signature was constructed based on these lncRNAs. This signature had a good predictive performance and was superior to TNM stages and previous clinical risk scores. I-131 therapy was found to be associated with favorable prognosis in patients with high-risk scores but not those with low-risk scores. Gene set enrichment analysis suggested that a series of hallmark gene sets were enriched in the high-risk subgroup. Single-cell RNA sequencing analysis suggested that the lncRNAs were mainly expressed in thyroid cells but not stromal cells. In conclusion, our study constructed a well-performed six-lncRNAs signature to predict PFI and I-131 therapy benefits in PTC.

1 Introduction

The incidence of thyroid cancers (TC), the most frequent endocrine tumors, has been increasing during the past decades [1,2], largely due to the progressively available and sensitive use of diagnostic technologies [3,4]. Differentiated thyroid cancer (DTC), originating from follicular epithelial cells [5], accounts for over 95% of all TC. Most DTC is papillary thyroid cancer (PTC; 85–90%) [6,7]. PTC typically responds well to standard therapy, including radical surgery, radioactive iodine (I-131) therapy, and endocrine therapy, and has a relatively good prognosis with a more than 90% 10-year survival rate. However, some patients experience recurrence after initial treatment [8]. Therefore, it is crucial to develop novel biomarkers or risk models to accurately evaluate the prognosis of PTC to ensure patients with low risk are not over-treated while those with high risk receive appropriate aggressive treatment.

Autophagy, a critical intracellular process, degrades or removes damaged or denatured proteins and dysfunctional organelles in lysosomes and is essential to maintain cellular homeostasis, metabolism, and survival [9,10]. Abnormal autophagy is involved in various diseases and associated with cancer occurrence, development, and metastasis although its definitive role in carcinogenesis and underlying mechanisms are inconclusive [11]. It plays either a protective role by inhibiting tumor development in the early stages or a detrimental role by promoting tumor progression in advanced stages of cancers. Additionally, autophagy can enhance tumor resistance to chemotherapy or radiotherapy [12]. In thyroid cancer, some studies have revealed autophagy can regulate tumor development and dedifferentiation and is involved in drug resistance. Kim et al. found autophagy-related proteins, LC3A, LC3B, p62, and BNIP-3, differ according to thyroid cancer subtypes [13]. Plantinga et al. found autophagy activity is associated with clinical response to radioiodine therapy potentially via maintaining tumor cell differentiation in non-medullary thyroid cancer [14]. Wang et al. found combining vemurafenib and autophagy inhibitors exerts more pronounced tumor suppression in thyroid cancer [15]. Tesselaar et al. found that digitalis-like compounds, the autophagy activators, can restore human sodium-iodide symporter (hNIS) expression and iodide uptake in thyroid cancer cells and may be a promising strategy to overcome radioactive iodide resistance [16].

Long non-coding RNAs (lncRNAs) function in a series of cellular processes in cancers such as cell proliferation, autophagy, and genomic stability by regulating gene expression via diverse mechanisms [17]. The role of lncRNAs in thyroid cancer has been revealed gradually. In thyroid cancer, previous studies have revealed that some lncRNAs are associated with cell proliferation, apoptosis, and autophagy [18]. For example, lncRNA OIP5-AS1, regulated by METTL14, can promote PTC progression by miR-98/ADAMTS8 signaling [19]. LncRNA FER1L4 can promote PTC malignancy by targeting miR-612/CDH4 axis [20]. LncRNA MIAT can promote PTC invasion via miR-150/EZH2 pathway [21]. LncRNA TNRC6C-AS1 can inhibit cell proliferation and promote apoptosis and autophagy via Hippo signaling pathway in thyroid cancer cells [22]. LncRNA MALAT1 knockdown can inhibit tumor migration and invasion while increasing autophagy via miR-200a-3p/FOXA1 axis in thyroid cancer cells [23]. Considering the molecular and clinical value of autophagy and lncRNAs in thyroid cancer, we here aimed to establish an effective autophagy-related lncRNAs risk signature to predict progression-free interval (PFI) in PTC patients.

2 Methods and materials

2.1 Data collection and processing

The workflow of this study is presented in Figure 1. The expression data including mRNA and lncRNA of PTC patients were got from TCGA database (https://www.cbioportal.org) [24]. This dataset consisted of 507 subjects, 510 tumor samples, and 58 adjacent normal tissue samples. Only the patients with clinical data and follow-up time/PFI ≥ 30 days were included. Overall, 498 cases were finally included and were randomly split into a training cohort (n = 299) and a validation cohort (n = 199). A total 222 autophagy-related genes (ARGs) were obtained from the Human Autophagy Database (HADb, http://autophagy.lu/clustering/index.html), which collected those genes from the literature. LncRNAs and ARGs mRNAs expression were extracted according to GENCODE annotations (https://www.gencodegenes.org). LncRNAs with zero expression levels in more than 50% of samples were excluded. Clinical variables including age, gender, race, cancer history, thyroid gland disorder history, histological types, TNM stages, T stage, N stage, M stage, tumor location, residual tumor, American Thyroid Association (ATA) risk stratification, the distant metastasis, patient age, completeness of resection, local invasion, and tumor size (MACIS) scores were collected. The methods of assessing the tumors with ATA risk stratification and MACIS scores have been described in the previous report [24] and are introduced in the Supplementary data. We also re-evaluated the tumors based on the methods. Additionally, BRAF mutation and telomerase reverse transcriptase (TERT) promoter mutation status information were extracted, which have been reported to be associated with prognosis in TC [25]. Patients’ progression-free survival (PFS) and overall survival (OS) were also extracted.

Figure 1 
                  Flowchart.
Figure 1

Flowchart.

2.2 Screening of the differentially expressed ARGs-related lncRNAs

We used the “Limma” package to identify the differentially expressed lncRNAs (DElncs) between tumor samples and control samples with the thresholds of fold change (FC) >1.5 or <1/1.5 and false discovery rate (FDR) <0.05 [26]. Subsequently, the correlation coefficient (R 2) between ARGs and DElncs was calculated by Pearson correlation analyses. The lncRNAs with R 2 > 0.25 and P < 0.001 were defined as autophagy-related lncRNAs.

2.3 Construction of autophagy-related lncRNAs signature

Univariate Cox regression analyses were performed to explore the associations of the autophagy-related DElncs with PFI. The autophagy-related DElncs that were associated with PFI in both the entire cohort and the training cohort (P < 0.1) were used as candidates to build the prognostic signature in the training cohort. The candidate DElncs were imputed into LASSO Cox regression analysis. The core autophagy-related DElncs tightly related to PFI were obtained when the optimal lambda value was achieved. The selected lncRNAs were then subjected to stepwise multivariate Cox regression analysis to build the autophagy-related lncRNAs risk signature. Each patient’s risk score was calculated by a linear combination of multiplying each lncRNA expression and the corresponding Cox regression coefficient. The patients were stratified into high-risk and low-risk groups by the median value. The PFI difference between the two groups was compared by Kaplan–Meier curves. Multivariate Cox regression analysis was performed to evaluate the independent prognostic value of the risk scores by adjusting the potential confounders. For the adjusted model I, the confounders was selected if they changed the effect estimate of the risk scores on PFI by more than 10% or were significantly associated with PFI. For the adjusted model II, the confounders in the adjusted model I and the remaining demographic data were adjusted. In addition, the area under the time-dependent ROC curves (AUC), as well as Harrell’s concordance index (C-index), were utilized to assess the predictive value of the autophagy-related lncRNAs risk signature. Validation was performed in the validation cohort and the entire cohort. The predictive performance of the risk model was also compared with TNM stages, ATA risk stratification, and MACIS scores by comparing their C-indices in the patients without missing values.

2.4 Association of the risk score with I-131 therapy efficacy

The association of the risk score with I-131 therapy efficacy was also explored with PFI as the primary endpoint and PFS and OS as the secondary endpoints. The patients were first divided into high and low risk with the optimal cutoff value, which was obtained based on the minimum P-value of the interaction test in univariate Cox analysis with PFI as the primary endpoint. I-131 therapy efficacy was investigated in patients with high or low risk by Kaplan–Meier curves with log-rank tests.

2.5 Gene set enrichment analysis (GSEA)

GSEA (version 3.0) was executed to assess the significantly different hallmark gene sets between different risk subgroups. The enriched gene sets whose/normalized enrichment score (NES)/> 1, nominal P-value <0.05, and FDR q-value <0.05 were treated as significant.

2.6 Single-cell RNA sequencing data analysis

The scRNA-seq data of 22 fresh surgical samples from six primary PTC tumors, six paired adjacent normal tissues, eight metastatic lymph nodes (LNs, including three recurrent LNs), and two subcutaneous metastatic loci were extracted from the GSE184362 dataset stored in the Gene Expression Omnibus (GEO) database [27]. Further analyses were performed with the R package, “Seurat,” using the standard data analysis pipeline [28]. Briefly, cells with low quality (the proportion of mitochondrial genes counts >10%, UMIs <500 or >5,000) were first removed; then the cell gene expression matrix was normalized and scaled with the default parameters; subsequently, the top 2,000 highly variable genes were identified by the FindVariableFeatures() function for the principal component analysis; fourth, the functions, FindNeighbors() and FindClusters() were utilized for cell clustering at a resolution of 0.4; next, the uniform manifold approximation and projection (UMAP) were used for visualization; cell markers provided in the previous report were used to annotate the cell clusters [27]. The cells with positive expression of lncRNAs of interest were assigned to the positive group and the other cells to the negative group. The differentially expressed genes between the two groups were identified through the FindAllMarkers() function, ranked by the log FC, and subjected to GSEA to explore the potential mechanisms.

2.7 Statistical analysis

R software v3.4.3 was utilized to perform all statistical analyses. The categorical variables were compared by chi-square tests or Fisher’s exact tests while the continuous variables were compared by Wilcox tests or Kruskal–Wallis tests. Kaplan–Meier curves were plotted to assess the prognosis differences between different groups stratified by risk scores and I-131 therapy efficacy. Cox regression analyses were carried out to identify lncRNAs that were associated with PFI. P < 0.05 was regarded as statistically significant unless otherwise stated.

  1. Ethics statement: The current study has been approved by the Institutional Review Board of Tianjin Union Medical Center of Nankai University (2021-B34).

3 Results

3.1 Identification of differentially expressed autophagy-related lncRNAs

The mRNA expression data of PTC tissues and adjacent normal tissues were obtained from the TCGA database. A total of 222 ARGs were generated from the HADb database and their expression data were extracted. About 14,822 lncRNAs were also extracted and 7,425 lncRNAs whose expressions were non-zero in more than 50% of samples were finally included. DElncs were identified between PTC and normal thyroid tissues via the “Limma” package. Based on the cutoff criteria, 262 DElncs with 178 lncRNAs downregulated and 84 lncRNAs upregulated were found. The volcano plot and heatmap for these 262 DElncs in tumor and adjacent tissues are displayed in Figure 2. Pearson correlation analyses were performed between these lncRNAs and 222 ARGs. Finally, 199 autophagy-related DElncs were attained based on the criteria of R 2 > 0.25 and P < 0.001.

Figure 2 
                  Comparison of lncRNAs expression in PTC tissues with adjacent tissues. (a) Volcano plot. The top ten DElncs were indicated based on the FDR, which were all downregulated. (b) Heatmap plot. Note: lfc, log2(FC).
Figure 2

Comparison of lncRNAs expression in PTC tissues with adjacent tissues. (a) Volcano plot. The top ten DElncs were indicated based on the FDR, which were all downregulated. (b) Heatmap plot. Note: lfc, log2(FC).

3.2 Establishment of autophagy-related lncRNA risk signature

Four hundred ninety-eight PTC patients were included and randomly divided into a training cohort (n = 299) and a validation cohort (n = 199). The clinical characteristics of the two cohorts were similar (Table 1). Univariate Cox regression analyses were performed to explore the associations of the autophagy-related DElncs with PFI in the entire cohort and training cohort. The autophagy-related DElncs that were associated with PFI in both the entire and training cohort (P < 0.1) were imputed into LASSO Cox regression analysis in the training cohort. Thirteen core lncRNAs tightly related to PFI were obtained at optimal lambda value (Figure 3). The selected lncRNAs were then subjected to stepwise multivariate Cox regression analysis. Finally, an autophagy-related lncRNAs risk signature consisting of six lncRNAs was constructed by multiplying each lncRNA expression and the corresponding Cox regression coefficient (Table 2). The six lncRNAs were co-expressed with 31 ARGs (R 2 > 0.25; Figure 4).

Table 1

Comparison of the clinical characteristics between the training and validation cohorts

Cohort Training Validation P-value
N 299 199
Age (years) 47.0 ± 15.4 48.0 ± 16.4 0.483
MACIS scores (N = 486, unknown = 12) 5.4 ± 1.5 5.4 ± 1.6 0.585
Gender 0.991
Female 218 (72.9%) 145 (72.9%)
Male 81 (27.1%) 54 (27.1%)
Race 0.553
White 197 (65.9%) 132 (66.3%)
Asian 35 (11.7%) 16 (8.0%)
Others 16 (5.4%) 12 (6.0%)
Unknown 51 (17.1%) 39 (19.6%)
Cancer history 0.832
No 278 (93.0%) 186 (93.5%)
Yes 21 (7.0%) 13 (6.5%)
Thyroid gland disorder history 0.489
No 162 (54.2%) 113 (56.8%)
Yes 98 (32.8%) 67 (33.7%)
Unknown 39 (13.0%) 19 (9.5%)
Histological types 0.838
Classical/usual 211 (70.6%) 141 (70.9%)
Follicular 59 (19.7%) 42 (21.1%)
Tall cell 24 (8.0%) 12 (6.0%)
Others 5 (1.7%) 4 (2.0%)
TNM stage 0.770
Stage I 168 (56.2%) 111 (55.8%)
Stage II 29 (9.7%) 23 (11.6%)
Stage III 68 (22.7%) 43 (21.6%)
Stage IV 32 (10.7%) 22 (11.1%)
Unknown 2 (0.7%) 0 (0.0%)
T stage 0.867
T1 86 (28.8%) 56 (28.1%)
T2 93 (31.1%) 69 (34.7%)
T3 104 (34.8%) 66 (33.2%)
T4 15 (5.0%) 7 (3.5%)
TX 1 (0.3%) 1 (0.5%)
N stage 0.245
N0 133 (44.5%) 95 (47.7%)
N1 140 (46.8%) 80 (40.2%)
NX 26 (8.7%) 24 (12.1%)
M stage 0.773
M0 172 (57.5%) 109 (54.8%)
M1 5 (1.7%) 4 (2.0%)
MX 121 (40.5%) 86 (43.2%)
Unknown 1 (0.3%) 0 (0.0%)
Tumor location 0.921
Left lobe 109 (36.5%) 65 (32.7%)
Right lobe 123 (41.1%) 87 (43.7%)
Bilateral 50 (16.7%) 36 (18.1%)
Isthmus 13 (4.3%) 9 (4.5%)
Unknown 4 (1.3%) 2 (1.0%)
Residual tumor 0.198
R0 226 (75.6%) 154 (77.4%)
R1 38 (12.7%) 14 (7.0%)
R2 3 (1.0%) 1 (0.5%)
RX 16 (5.4%) 14 (7.0%)
Unknown 16 (5.4%) 16 (8.0%)
ATA risk stratification 0.166
Low 97 (32.4%) 73 (36.7%)
Intermediate 179 (59.9%) 112 (56.3%)
High 20 (6.7%) 8 (4.0%)
Unknown 3 (1.0%) 6 (3.0%)
BRAF mutation 0.191
No 115 (38.5%) 82 (41.2%)
Yes 178 (59.5%) 108 (54.3%)
Unknown 6 (2.0%) 9 (4.5%)
TERT mutation 0.862
No 202 (67.6%) 138 (69.3%)
Yes 23 (7.7%) 13 (6.5%)
Unknown 74 (24.7%) 48 (24.1%)

Note: ATA, American Thyroid Association.

Figure 3 
                  Screening of the core autophagy-related lncRNAs associated with PFI by LASSO COX regression analysis. (a) LASSO coefficient of the lncRNAs by 10-fold cross-validation. (b) Partial likelihood deviance with corresponding log(λ) values at the minimal deviance.
Figure 3

Screening of the core autophagy-related lncRNAs associated with PFI by LASSO COX regression analysis. (a) LASSO coefficient of the lncRNAs by 10-fold cross-validation. (b) Partial likelihood deviance with corresponding log(λ) values at the minimal deviance.

Table 2

Information of the lncRNAs in the autophagy-related lncRNAs risk model for predicting PFI of patients with papillary thyroid carcinoma

ENSG ID Symbol Chromosome Gene start (bp) Gene end (bp) Coefficient P-value
ENSG00000254153 CTA-398F10.2 8 8456909 8461337 −1.892 0.002
ENSG00000232453 RP4-794H19.1 1 58882868 58931897 1.178 0.015
ENSG00000250073 RP11-677M14.3 11 124759129 124766119 −1.381 0.009
ENSG00000259264 RP11-60L3.1 15 74202705 74221555 0.440 0.037
ENSG00000229116 RP11-20J15.3 10 44282489 44293998 −0.840 0.003
ENSG00000259042 AE000661.50 14 22415362 22418657 −0.544 0.074
Figure 4 
                  Correlation of the selected six lncRNAs with ARGs.
Figure 4

Correlation of the selected six lncRNAs with ARGs.

3.3 Validation of the autophagy-related lncRNA risk signature

The prognostic performance of the autophagy-related lncRNAs risk model was further assessed by Kaplan–Meier plotting curves and time-dependent ROC curves in the training, validation, and entire cohorts. The patients in these cohorts were split into high-risk and low-risk groups with the median value of the risk scores. In the training cohort, the risk curve and the scatterplot showed that the low-risk group had lower risk scores and progression rates (Figure 5a). The corresponding expression profiles of the six autophagy-related lncRNAs were also visualized by heatmap (Figure 5a). The AUCs of 1-, 3-, and 5-year PFI were 0.78, 0.79, and 0.76, respectively (Figure 5b). Kaplan–Meier curve indicated that patients with low risk exerted more favorable PFI than those with high risk (P < 0.0001, Figure 5c). In the validation cohort, the risk score distribution, progression status, and the corresponding expression profiles of the six lncRNAs were also determined (Figure 6a). The AUCs of 1-, 3-, and 5-year PFI were 0.64, 0.71, and 0.79, respectively (Figure 6b). Kaplan–Meier curve showed that patients with low risk exerted more favorable PFI than those with high risk (P = 0.0033, Figure 6c). Similar results were identified in the entire cohort (Figure 7). The Harrell’s C-indices (95% CIs) of the six lncRNAs risk signature were 0.776 (0.692, 0.861), 0.717 (0.593, 0.841), and 0.756 (0.686, 0.825) in the training, validation, and entire cohorts, respectively. The predictive performance of the lncRNAs risk model was also compared with TNM stages, ATA risk stratification, and MACIS scores. In the patients without missing values (n = 480), Harrell’s C-index of the six lncRNAs signature was 0.759 and bigger than that of TNM stages, ATA risk stratification, and MACIS scores, whose C-indices were 0.631, 0.651, and 0.646, respectively (all P < 0.05; Table S1). Collectively, these results revealed that the risk signature exhibited good performance to predict the PFI of the PTC patients, and was superior to TNM stages, ATA risk stratification, and MACIS scores.

Figure 5 
                  Predictive performance of the six autophagy-related lncRNAs signature in the training cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value.
Figure 5

Predictive performance of the six autophagy-related lncRNAs signature in the training cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value.

Figure 6 
                  Predictive performance of the six autophagy-related lncRNAs signature in the validation cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value of the risk scores.
Figure 6

Predictive performance of the six autophagy-related lncRNAs signature in the validation cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value of the risk scores.

Figure 7 
                  Predictive performance of the six autophagy-related lncRNAs signature in the entire cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value of the risk scores.
Figure 7

Predictive performance of the six autophagy-related lncRNAs signature in the entire cohort. (a) Risk scores, PFI/progression status, and expression heatmap. (b) Time-dependent ROC curves for predicting 1-, 3-, and 5-year PFI based on the risk scores. (c) Kaplan–Meier curves of the patients with high and low-risk scores, which were divided by the median value of the risk scores.

Finally, multivariate analysis results suggested that, whether in the training, validation, or entire cohorts, the six-lncRNAs risk scores (serve as a continuous variable) were an independent prognostic factor (Table 3). When the risk scores were equally split into two groups or three groups, high-risk scores were also identified as an independent prognostic factor.

Table 3

Univariate/multivariate COX regression analyses of the associations of autophagy-related lncRNAs risk scores with the PFI of patients with papillary thyroid carcinoma

Cohort/subgroups Non-adjusted Adjust I Adjust II
HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value
Risk scores as a continuous variable
Training cohort 2.706 (1.935, 3.783) <0.001 2.532 (1.690, 3.792) <0.001 2.544 (1.699, 3.807) <0.001
Validation cohort 1.794 (1.195, 2.694) 0.005 2.125 (1.157, 3.902) 0.015 2.575 (1.325, 5.004) 0.005
Entire cohort 2.303 (1.768, 2.999) <0.001 2.082 (1.552, 2.794) <0.001 2.128 (1.589, 2.851) <0.001
Risk scores as a categorical variable (two groups)
Training cohort
 Low 1 1 1
 High 11.285 (3.449, 36.923) <0.001 9.191 (2.685, 31.469) <0.001 10.898 (3.078, 38.581) <0.001
Validation cohort
 Low 1 1 1
 High 5.233 (1.503, 18.218) 0.009 5.917 (1.375, 25.471) 0.017 9.547 (1.774, 51.381) 0.009
Entire cohort
 Low 1 1 1
 High 8.061 (3.439, 18.897) <0.001 6.490 (2.694, 15.632) <0.001 7.089 (2.917, 17.226) <0.001
Risk scores as a categorical variable (three groups)
Training cohort 1 1 1
 Low 1 1 1
 Medium 4.676 (1.010, 21.644) 0.049 4.683 (0.977, 22.454) 0.054 5.226 (1.063, 25.698) 0.042
 High 12.279 (2.895, 52.088) 0.001 9.692 (2.116, 44.392) 0.003 11.256 (2.403, 52.714) 0.002
Validation cohort
 Low 1 1 1
 Medium 4.585 (0.512, 41.054) 0.173 4.130 (0.333, 51.205) 0.270 3.224 (0.261, 39.860) 0.362
 High 13.257 (1.723, 101.986) 0.013 20.141 (1.777, 228.324) 0.015 34.465 (2.903, 409.094) 0.005
Entire cohort
 Low 1 1 1
 Medium 7.013 (1.582, 31.082) 0.010 6.771 (1.495, 30.673) 0.013 7.503 (1.629, 34.550) 0.010
 High 20.216 (4.867, 83.973) <0.001 16.905 (3.920, 72.898) <0.001 19.531 (4.428, 86.148) <0.001

Note: adjust I: adjust for age; histological types: TNM stage, T stage, N stage, M stage; tumor location: BRAF mutation and TERT mutation; adjust II: adjust for age, gender, race; histological types: TNM stage, T stage, N stage, M stage; tumor location: BRAF mutation and TERT mutation; HR, hazard ratio; CI, confidence interval.

3.4 Associations of the risk scores with clinical features and BRAF/TERT mutation status in PTC patients

The associations of the six-lncRNAs risk scores with patients’ clinical features and BRAF/TERT mutation status were analyzed in the entire cohort. Patients were divided into high and low-risk groups by the median value and the features in different risk groups were compared. The results suggested that the patients with high risk had a higher prevalence of tall cell carcinomas, advanced stages, high ATA risk stratification, and higher TERT promoter mutation rate (Table 4). Comparisons of the original risk scores in patients with different features suggested patients with tall cell carcinomas (Figure 8a), III–IV stages (Figure 8b), high ATA risk stratification (Figure 8c), mutated BRAF (Figure 8d), or mutated TERT promoter (Figure 8e) had higher risk scores. The lncRNAs signature risk scores were also found to be positively correlated with MACIS scores (Figure 8f).

Table 4

Comparison of the clinical characteristics between the patients with low and high-risk scores

LncRNA signature scores Low High P-value
N 249 249
Age (years) 47.0 ± 15.3 47.7 ± 16.3 0.622
MACIS scores (N = 486) 5.2 ± 1.4 5.6 ± 1.6 <0.001
Gender 0.614
Female 179 (71.9%) 184 (73.9%)
Male 70 (28.1%) 65 (26.1%)
Race 0.536
White 160 (64.3%) 169 (67.9%)
Asian 29 (11.6%) 22 (8.8%)
Others 12 (4.8%) 16 (6.4%)
Unknown 48 (19.3%) 42 (16.9%)
Cancer history 0.286
No 229 (92.0%) 235 (94.4%)
Yes 20 (8.0%) 14 (5.6%)
Thyroid gland disorder history 0.174
No 128 (51.4%) 147 (59.0%)
Yes 92 (36.9%) 73 (29.3%)
Unknown 29 (11.6%) 29 (11.6%)
Histological types 0.009
Classical/usual 181 (72.7%) 171 (68.7%)
Follicular 56 (22.5%) 45 (18.1%)
Tall cell 9 (3.6%) 27 (10.8%)
Others 3 (1.2%) 6 (2.4%)
TNM stage 0.022
Stage I 152 (61.0%) 127 (51.0%)
Stage II 27 (10.8%) 25 (10.0%)
Stage III 50 (20.1%) 61 (24.5%)
Stage IV 18 (7.2%) 36 (14.5%)
Unknown 2 (0.8%) 0 (0.0%)
T stage <0.001
T1 96 (38.6%) 46 (18.5%)
T2 79 (31.7%) 83 (33.3%)
T3 68 (27.3%) 102 (41.0%)
T4 5 (2.0%) 17 (6.8%)
TX 1 (0.4%) 1 (0.4%)
N stage 0.062
N0 124 (49.8%) 104 (41.8%)
N1 97 (39.0%) 123 (49.4%)
NX 28 (11.2%) 22 (8.8%)
M stage 0.281
M0 141 (56.6%) 140 (56.2%)
M1 2 (0.8%) 7 (2.8%)
MX 105 (42.2%) 102 (41.0%)
Unknown 1 (0.4%) 0 (0.0%)
Tumor location 0.005
Left lobe 96 (38.6%) 78 (31.3%)
Right lobe 100 (40.2%) 110 (44.2%)
Bilateral 47 (18.9%) 39 (15.7%)
Isthmus 3 (1.2%) 19 (7.6%)
Unknown 3 (1.2%) 3 (1.2%)
Residual tumor 0.253
R0 195 (78.3%) 185 (74.3%)
R1 19 (7.6%) 33 (13.3%)
R2 2 (0.8%) 2 (0.8%)
RX 18 (7.2%) 12 (4.8%)
Unknown 15 (6.0%) 17 (6.8%)
ATA risk stratification <0.001
Low 108 (43.4%) 62 (24.9%)
Intermediate 132 (53.0%) 159 (63.9%)
High 6 (2.4%) 22 (8.8%)
Unknown 3 (1.2%) 6 (2.4%)
BRAF mutation 0.157
No 109 (43.8%) 88 (35.3%)
Yes 133 (53.4%) 153 (61.4%)
Unknown 7 (2.8%) 8 (3.2%)
TERT promoter mutation 0.017
No 179 (71.9%) 161 (64.7%)
Yes 10 (4.0%) 26 (10.4%)
Unknown 60 (24.1%) 62 (24.9%)

Note: ATA, American Thyroid Association.

Figure 8 
                  Comparison of the risk scores in patients with different features. (a) Comparison of the risk scores in patients with different histological types. (b) Comparison of the risk scores in patients with different TNM stages. (c) Comparison of the risk scores in patients with different ATA risk stratification. (d) Comparison of the risk scores in patients with or without BRAF mutation. (e) Comparison of the risk scores in patients with or without TERT mutation   (f) Correlation of the risk scores and MACIS scores. Note: ns, not significant; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.
Figure 8

Comparison of the risk scores in patients with different features. (a) Comparison of the risk scores in patients with different histological types. (b) Comparison of the risk scores in patients with different TNM stages. (c) Comparison of the risk scores in patients with different ATA risk stratification. (d) Comparison of the risk scores in patients with or without BRAF mutation. (e) Comparison of the risk scores in patients with or without TERT mutation (f) Correlation of the risk scores and MACIS scores. Note: ns, not significant; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

3.5 Association of the risk scores with I-131 therapy benefits

Of the 390 patients, 215 (60%) with treatment information received postoperative I-131 therapy. No significant association of I-131 therapy with patients’ PFI was identified (P = 0.51; Figure 9a). However, when the patients were equally split into three groups by the LncRNAs signature risk scores, we observed a trend that I-131 therapy was associated with favorable PFI in the high-risk group while being associated with poor PFI in the low-risk group (data not shown), suggesting that the patients with high-risk scores but not those with low-risk scores might get benefit from I-131 therapy. To identify the patients favorable to I-131 therapy, the patients were divided into two groups (unfavorable group and favorable group) by a series of cutoff values, and the optimal cutoff value was selected by minimal P-value for interaction tests in univariate Cox analyses. One hundred ten (28%) and 280 (72%) patients in favorable (with high-risk scores) and unfavorable (with low-risk scores) groups were identified, respectively. As expected, I-131 therapy was associated with poor PFI in patients from the unfavorable group (P = 0.23; Figure 9b) while being associated with improved PFI in patients from the favorable group (P = 0.057; Figure 9c) although the associations did not reach statistical significance. We also used PFS and OS as secondary endpoints to explore the effects of the risk groups on I-131 therapy benefits. Although improved PFS was observed after I-131 therapy in whole 390 patients, the PFS difference was not significant (P = 0.34; Figure 9d). Similarly, a trend of an association between I-131 therapy and poor PFS was found in the unfavorable group (P = 0.15; Figure 9e) and I-131 therapy was significantly associated with improved PFS in the favorable group (P = 0.02; Figure 9f). As for OS (Figure 9g–i), I-131 therapy exerted no effects on OS in patients from the unfavorable group (P = 0.88; Figure 9h). In the favorable group, no death occurred in patients with I-131 therapy (P = 0.012; Figure 9i).

Figure 9 
                  Comparisons of PFI, PFS, and OS between PTC patients with and without postoperative I-131 therapy in whole cohort or subgroups stratified by the risk scores. (a–c) Effects of I-131 therapy on PFI in the whole cohort (a), unfavorable subgroup (b), and favorable subgroup (c). (d–f) Effects of I-131 therapy on PFS in the whole cohort (d), unfavorable subgroup (e), and favorable subgroup (f). (g–i) Effects of I-131 therapy on OS in the whole cohort (g), unfavorable subgroup (h), and favorable subgroup (i).
Figure 9

Comparisons of PFI, PFS, and OS between PTC patients with and without postoperative I-131 therapy in whole cohort or subgroups stratified by the risk scores. (a–c) Effects of I-131 therapy on PFI in the whole cohort (a), unfavorable subgroup (b), and favorable subgroup (c). (d–f) Effects of I-131 therapy on PFS in the whole cohort (d), unfavorable subgroup (e), and favorable subgroup (f). (g–i) Effects of I-131 therapy on OS in the whole cohort (g), unfavorable subgroup (h), and favorable subgroup (i).

3.6 GSEA

GSEA was conducted to identify the differentially enriched hallmark gene sets between the high-risk (upper tertile) and low-risk (lower tertile) groups. The gene expression profiles of the high-risk and low-risk groups were compared and subjected to GSEA against hallmark gene sets. The results revealed that 21 gene sets were enriched in the high-risk group while none were enriched in the low-risk group based on the cut criteria. The top ten enriched gene sets in the high-risk group were allograft rejection, interferon-gamma response, inflammatory response, interferon-alpha response, epithelial–mesenchymal transition, coagulation, IL6 JAK STAT3 signaling, Kras signaling up, complement, and IL2 STAT5 signaling (Figure 10).

Figure 10 
                  Top enriched hallmark gene sets between high and low-risk groups determined by GSEA: (a) allograft rejection, (b) interferon-gamma response, (c) inflammatory response, (d) interferon-alpha response, (e) epithelial–mesenchymal transition, (f) coagulation, (g) IL6 JAK STAT3 signaling, (h) Kras signaling up, (i) complement, and (j) IL2 STAT5 signaling.
Figure 10

Top enriched hallmark gene sets between high and low-risk groups determined by GSEA: (a) allograft rejection, (b) interferon-gamma response, (c) inflammatory response, (d) interferon-alpha response, (e) epithelial–mesenchymal transition, (f) coagulation, (g) IL6 JAK STAT3 signaling, (h) Kras signaling up, (i) complement, and (j) IL2 STAT5 signaling.

3.7 Exploration of the lncRNAs in PTC at a single-cell level

scRNA-seq data of 22 samples were extracted from the GEO database including six primary PTC tumors, six paired adjacent normal tissues, five initially treated involved LNs, three recurrent LNs, and two subcutaneous metastases. After quality control, a total of 156,295 cells remained for further analyses. Six main cell populations including B cells, endothelial cells, fibroblasts, myeloid cells, T/natural killer cells, and thyroid cells were identified according to corresponding markers (Figure 11a). The six lncRNAs in our signature were mainly expressed in thyroid cells (Figure 11b), then only thyroid cells were selected for subsequent analyses. The proportion of CTA-398F10.2, RP4-794H19.1, RP11-677M14.3 positive cells in tumor and LN samples was lower than that in adjacent normal samples while the proportion of RP11-60L3.1, RP11-20J15.3, and AE000661.50 positive cells was higher (Figure 11c), similar with the results from TCGA bulk RNA sequencing (Figure S1). The thyroid cells could be further clustered into nine subgroups, which included three developmental hierarchies (State 1–3) based on the cell markers derived from trajectory analysis in the previous report [27]. State 1 indicated the normal thyroid cells, state 2 indicated the premalignant cells, and state 3 indicated the malignant cells. Thyroid cells in adjacent normal samples were all with states 1 and 2 whereas cells with state 3 were enriched in the tumor, LN, and distance metastasis samples (Figure 11d). CTA-398F10.2, RP4-794H19.1, and RP11-677M14.3 were mainly expressed in states 1 and 2 cells while RP11-60L3.1, RP11-20J15.3, and AE000661.50 were mainly expressed in state 3 cells (Figure 11e). Furthermore, we performed hallmark pathway enrichment analyses between the thyroid cells with positive or negative LncRNA expression (Figure 12). A series of hallmark pathways were negatively associated with CTA-398F10.2, RP4-794H19.1, and RP11-677M14.3 expression, including angiogenesis, apoptosis, cholesterol homeostasis, coagulation, complement, epithelial–mesenchymal transition, IL2 STAT5 signaling, inflammatory response, interferon-alpha response, interferon-gamma response, Kras signaling up, P53 pathway, and TNFα signaling via NFκB. Most of those pathways were positively associated with RP11-60L3.1 expression. And only several pathways were significantly associated with RP11-20J15.3 and AE000661.50 expression.

Figure 11 
                  Exploration of the lncRNAs in papillary thyroid carcinoma at a single cell level. Single-cell RNA sequencing data of 22 samples from PTC patients were obtained from the GSE184362 dataset. (a) Cells were clustered into six main types and visualized by UMAP plot. (b) Proportions of the cells with different lncRNAs expression in different cell clusters. (c) Proportions of the thyroid cells with different lncRNAs expression in different sample types. P, paired adjacent normal tissues; T, primary tumors; LN, initially treated involved lymph nodes; rLN, recurrent LN; rSC, recurrent subcutaneous metastases. (d) Proportions of the thyroid cells with different state statuses in different sample types. (e) Proportions of the thyroid cells with different lncRNAs expression in different state cells.
Figure 11

Exploration of the lncRNAs in papillary thyroid carcinoma at a single cell level. Single-cell RNA sequencing data of 22 samples from PTC patients were obtained from the GSE184362 dataset. (a) Cells were clustered into six main types and visualized by UMAP plot. (b) Proportions of the cells with different lncRNAs expression in different cell clusters. (c) Proportions of the thyroid cells with different lncRNAs expression in different sample types. P, paired adjacent normal tissues; T, primary tumors; LN, initially treated involved lymph nodes; rLN, recurrent LN; rSC, recurrent subcutaneous metastases. (d) Proportions of the thyroid cells with different state statuses in different sample types. (e) Proportions of the thyroid cells with different lncRNAs expression in different state cells.

Figure 12 
                  Hallmark pathways enrichment analysis between the thyroid cells with or without the corresponding LncRNA expression based on the single-cell RNA sequencing dataset GSE184362. NES, normalized enrichment score.
Figure 12

Hallmark pathways enrichment analysis between the thyroid cells with or without the corresponding LncRNA expression based on the single-cell RNA sequencing dataset GSE184362. NES, normalized enrichment score.

4 Discussion

In the present study, we identified 199 autophagy-related lncRNAs in PTC and constructed a novel six-lncRNAs risk signature to predict patients’ PFI based on these lncRNAs. LncRNAs usually function by epigenetically regulated gene expression at different levels, including chromatin, gene splicing, transcription, and post-transcription. Although the function and clinical significance of the lncRNAs in thyroid cancer are not well understood, some of them have been identified to be involved in the autophagy process and thus affect the development and progression of thyroid cancer. Wang et al. have found lncRNA BANCR expression is upregulated in PTC and increases cell proliferation and activates autophagy [29]. Yang et al. have found that lncRNA TNRC6C-AS1 can downregulate STK4 methylation through the Hippo signaling pathway and then inhibit cell proliferation while promoting apoptosis and autophagy in thyroid cancer cells [22]. Zhao et al. have found that silencing lncRNA RP11-476D10.1 can inhibit cell proliferation while increasing apoptosis and autophagy of PTC cells through microRNA-138-5p-dependent inhibition of LRRK2 [30]. Gou et al. have found that lncRNA MALAT1 knockdown inhibits cell proliferation, migration, and invasion while increasing cell apoptosis and autophagy in thyroid cancer cells partly via the ceRNA network of MALAT1/miR-200a-3p/FOXA1 [23]. Gugnoni et al. have found that lncRNA LINC00941 can modulate cytoskeleton architecture and autophagy via regulating CDH6 in thyroid cancer cells [31]. Qin et al. have found that lncRNA GAS8-AS1, induced by ATF2, can promote autophagy by targeting miR-187-3p/ATG5 and miR-1343-3p/ATG7 axes in thyroid cancer cells [32]. Wen et al. have found that lncRNA SNHG9 inhibits cell autophagy whereas promotes cell apoptosis by YBOX3/P21 pathway in normal thyroid epithelial cells [33]. Peng et al. have found that lncRNA SLC26A4-AS1 overexpression can recruit ETS1 to promote ITPR1 expression and thereby promote autophagy and alleviate PTC progression [34]. In our study, we have identified 262 lncRNAs that were differently expressed in PTC compared to adjacent normal controls, and among them, 199 lncRNAs were negatively or positively correlated with ARGs (R 2 > 0.25). Then we utilized a series of methods, including univariate, LASSO, and stepwise multivariate COX regression analyses to establish a prognostic model based on the autophagy-related lncRNAs to predict PFI of PTC patients. The prognostic model consisted of six lncRNAs including CTA-398F10.2, RP4-794H19.1, RP11-677M14.3, RP11-60L3.1, RP11-20J15.3, and AE000661.50. The model was superior to TNM stages, ATA risk stratification, MACIS scores, and the previous model constructed with the key differentially expressed mRNAs regulated by differentially expressed circular RNAs [35]. In addition, we found that postoperative I-131 therapy was associated with favorable PFI, PFS, and OS in patients with high lncRNAs signature risk scores but not in those with low-risk scores, suggesting that the risk scores might be used to identify the patients who benefited from I-131 therapy and reduce unnecessary I-131 administration. Currently, the functions of our included lncRNAs in cancer were not clear. Gong et al. have found that CTA-398F10.2 can be increased by radiation in glioma cells but not in normal astrocytes [36]. Li et al. have found that the proto-oncogene JUN is correlated with RP4-794H19.1 and contributes to TNF signaling pathway in nasopharyngeal cancer [37]. James et al. have found that RP11-677M14.3 is associated with the different molecular subtypes of B cell acute lymphoblastic leukemia and co-occurrent with TGFB2 expression [38]. Liu et al. have mined data from 239 bladder cancer patients from TCGA database and constructed a multidimensional transcriptome signature including RP11-60L3.1 to predict the patient’s prognosis [39]. The molecular mechanisms of these lncRNAs in thyroid cancer need further investigation. By analyzing the single-cell RNA sequencing data, we found that the six lncRNAs were mainly expressed in thyroid cells but not stromal cells. Specifically, CTA-398F10.2, RP4-794H19.1, and RP11-677M14.3 were mainly expressed in normal and premalignant thyroid cells while RP11-60L3.1, RP11-20J15.3, and AE000661.50 were mainly expressed in malignant thyroid cells.

This is the first study to construct an autophagy-related lncRNAs signature, which shows a favorable prognostic performance. Nonetheless, there are still several limitations in the present study. First, the prognostic model was constructed based only on the data from TCGA database, and external validation with independent cohorts is needed. Second, further in vitro and in vivo research should be performed to investigate the molecular mechanisms and interrelation of these six lncRNAs in thyroid cancer.

5 Conclusion

In the present study, we identified 199 autophagy-related lncRNAs in PTC. Based on these lncRNAs, we constructed a novel six lncRNAs risk signature to predict the PFI of PTC patients, which exerts a good predictive performance in the training cohort as well as the validation cohort and is superior to TNM stages, ATA risk stratification, and MACIS scores.


tel: +86-22-27557171; fax: +18920392330
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  1. Funding information: This study was supported by the hospital-level project of Tianjin People’s Hospital (2021YJ004), Tianjin Medical Key Discipline (Specialty) Construction Project (TJYXZDXK-058B), and Tianjin Health Research Project.

  2. Conflict of interest: The authors declare no conflict of interest.

  3. Data availability statement: The datasets used or analyzed during this study are available from the TCGA and GEO databases.

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Received: 2022-06-15
Revised: 2022-12-17
Accepted: 2023-01-16
Published Online: 2023-03-03

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  42. CircRANBP17 modulated KDM1A to regulate neuroblastoma progression by sponging miR-27b-3p
  43. Exosomal miR-93-5p regulated the progression of osteoarthritis by targeting ADAMTS9
  44. Downregulation of RBM17 enhances cisplatin sensitivity and inhibits cell invasion in human hypopharyngeal cancer cells
  45. HDAC5-mediated PRAME regulates the proliferation, migration, invasion, and EMT of laryngeal squamous cell carcinoma via the PI3K/AKT/mTOR signaling pathway
  46. The association between sleep duration, quality, and nonalcoholic fatty liver disease: A cross-sectional study
  47. Myostatin silencing inhibits podocyte apoptosis in membranous nephropathy through Smad3/PKA/NOX4 signaling pathway
  48. A novel long noncoding RNA AC125257.1 facilitates colorectal cancer progression by targeting miR-133a-3p/CASC5 axis
  49. Impact of omicron wave and associated control measures in Shanghai on health management and psychosocial well-being of patients with chronic conditions
  50. Clinicopathological characteristics and prognosis of young patients aged ≤45 years old with non-small cell lung cancer
  51. TMT-based comprehensive proteomic profiling identifies serum prognostic signatures of acute myeloid leukemia
  52. The dose limits of teeth protection for patients with nasopharyngeal carcinoma undergoing radiotherapy based on the early oral health-related quality of life
  53. miR-30b-5p targeting GRIN2A inhibits hippocampal damage in epilepsy
  54. Long non-coding RNA AL137789.1 promoted malignant biological behaviors and immune escape of pancreatic carcinoma cells
  55. IRF6 and FGF1 polymorphisms in non-syndromic cleft lip with or without cleft palate in the Polish population
  56. Comprehensive analysis of the role of SFXN family in breast cancer
  57. Efficacy of bronchoscopic intratumoral injection of endostar and cisplatin in lung squamous cell carcinoma patients underwent conventional chemoradiotherapy
  58. Silencing of long noncoding RNA MIAT inhibits the viability and proliferation of breast cancer cells by promoting miR-378a-5p expression
  59. AG1024, an IGF-1 receptor inhibitor, ameliorates renal injury in rats with diabetic nephropathy via the SOCS/JAK2/STAT pathway
  60. Downregulation of KIAA1199 alleviated the activation, proliferation, and migration of hepatic stellate cells by the inhibition of epithelial–mesenchymal transition
  61. Exendin-4 regulates the MAPK and WNT signaling pathways to alleviate the osteogenic inhibition of periodontal ligament stem cells in a high glucose environment
  62. Inhibition of glycolysis represses the growth and alleviates the endoplasmic reticulum stress of breast cancer cells by regulating TMTC3
  63. The function of lncRNA EMX2OS/miR-653-5p and its regulatory mechanism in lung adenocarcinoma
  64. Tectorigenin alleviates the apoptosis and inflammation in spinal cord injury cell model through inhibiting insulin-like growth factor-binding protein 6
  65. Ultrasound examination supporting CT or MRI in the evaluation of cervical lymphadenopathy in patients with irradiation-treated head and neck cancer
  66. F-box and WD repeat domain containing 7 inhibits the activation of hepatic stellate cells by degrading delta-like ligand 1 to block Notch signaling pathway
  67. Knockdown of circ_0005615 enhances the radiosensitivity of colorectal cancer by regulating the miR-665/NOTCH1 axis
  68. Long noncoding RNA Mhrt alleviates angiotensin II-induced cardiac hypertrophy phenotypes by mediating the miR-765/Wnt family member 7B pathway
  69. Effect of miR-499-5p/SOX6 axis on atrial fibrosis in rats with atrial fibrillation
  70. Cholesterol induces inflammation and reduces glucose utilization
  71. circ_0004904 regulates the trophoblast cell in preeclampsia via miR-19b-3p/ARRDC3 axis
  72. NECAB3 promotes the migration and invasion of liver cancer cells through HIF-1α/RIT1 signaling pathway
  73. The poor performance of cardiovascular risk scores in identifying patients with idiopathic inflammatory myopathies at high cardiovascular risk
  74. miR-2053 inhibits the growth of ovarian cancer cells by downregulating SOX4
  75. Nucleophosmin 1 associating with engulfment and cell motility protein 1 regulates hepatocellular carcinoma cell chemotaxis and metastasis
  76. α-Hederin regulates macrophage polarization to relieve sepsis-induced lung and liver injuries in mice
  77. Changes of microbiota level in urinary tract infections: A meta-analysis
  78. Identification of key enzalutamide-resistance-related genes in castration-resistant prostate cancer and verification of RAD51 functions
  79. Falls during oxaliplatin-based chemotherapy for gastrointestinal malignancies – (lessons learned from) a prospective study
  80. Outcomes of low-risk birth care during the Covid-19 pandemic: A cohort study from a tertiary care center in Lithuania
  81. Vitamin D protects intestines from liver cirrhosis-induced inflammation and oxidative stress by inhibiting the TLR4/MyD88/NF-κB signaling pathway
  82. Integrated transcriptome analysis identifies APPL1/RPS6KB2/GALK1 as immune-related metastasis factors in breast cancer
  83. Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme
  84. Circular RNA Circ_0038467 promotes the maturation of miRNA-203 to increase lipopolysaccharide-induced apoptosis of chondrocytes
  85. An economic evaluation of fine-needle cytology as the primary diagnostic tool in the diagnosis of lymphadenopathy
  86. Midazolam impedes lung carcinoma cell proliferation and migration via EGFR/MEK/ERK signaling pathway
  87. Network pharmacology combined with molecular docking and experimental validation to reveal the pharmacological mechanism of naringin against renal fibrosis
  88. PTPN12 down-regulated by miR-146b-3p gene affects the malignant progression of laryngeal squamous cell carcinoma
  89. miR-141-3p accelerates ovarian cancer progression and promotes M2-like macrophage polarization by targeting the Keap1-Nrf2 pathway
  90. lncRNA OIP5-AS1 attenuates the osteoarthritis progression in IL-1β-stimulated chondrocytes
  91. Overexpression of LINC00607 inhibits cell growth and aggressiveness by regulating the miR-1289/EFNA5 axis in non-small-cell lung cancer
  92. Subjective well-being in informal caregivers during the COVID-19 pandemic
  93. Nrf2 protects against myocardial ischemia-reperfusion injury in diabetic rats by inhibiting Drp1-mediated mitochondrial fission
  94. Unfolded protein response inhibits KAT2B/MLKL-mediated necroptosis of hepatocytes by promoting BMI1 level to ubiquitinate KAT2B
  95. Bladder cancer screening: The new selection and prediction model
  96. circNFATC3 facilitated the progression of oral squamous cell carcinoma via the miR-520h/LDHA axis
  97. Prone position effect in intensive care patients with SARS-COV-2 pneumonia
  98. Clinical observation on the efficacy of Tongdu Tuina manipulation in the treatment of primary enuresis in children
  99. Dihydroartemisinin ameliorates cerebral I/R injury in rats via regulating VWF and autophagy-mediated SIRT1/FOXO1 pathway
  100. Knockdown of circ_0113656 assuages oxidized low-density lipoprotein-induced vascular smooth muscle cell injury through the miR-188-3p/IGF2 pathway
  101. Low Ang-(1–7) and high des-Arg9 bradykinin serum levels are correlated with cardiovascular risk factors in patients with COVID-19
  102. Effect of maternal age and body mass index on induction of labor with oral misoprostol for premature rupture of membrane at term: A retrospective cross-sectional study
  103. Potential protective effects of Huanglian Jiedu Decoction against COVID-19-associated acute kidney injury: A network-based pharmacological and molecular docking study
  104. Clinical significance of serum MBD3 detection in girls with central precocious puberty
  105. Clinical features of varicella-zoster virus caused neurological diseases detected by metagenomic next-generation sequencing
  106. Collagen treatment of complex anorectal fistula: 3 years follow-up
  107. LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through down-regulating SP-A by sponging to miR-424
  108. Efficacy analysis of empirical bismuth quadruple therapy, high-dose dual therapy, and resistance gene-based triple therapy as a first-line Helicobacter pylori eradication regimen – An open-label, randomized trial
  109. SMOC2 plays a role in heart failure via regulating TGF-β1/Smad3 pathway-mediated autophagy
  110. A prospective cohort study of the impact of chronic disease on fall injuries in middle-aged and older adults
  111. circRNA THBS1 silencing inhibits the malignant biological behavior of cervical cancer cells via the regulation of miR-543/HMGB2 axis
  112. hsa_circ_0000285 sponging miR-582-3p promotes neuroblastoma progression by regulating the Wnt/β-catenin signaling pathway
  113. Long non-coding RNA GNAS-AS1 knockdown inhibits proliferation and epithelial–mesenchymal transition of lung adenocarcinoma cells via the microRNA-433-3p/Rab3A axis
  114. lncRNA UCA1 regulates miR-132/Lrrfip1 axis to promote vascular smooth muscle cell proliferation
  115. Twenty-four-color full spectrum flow cytometry panel for minimal residual disease detection in acute myeloid leukemia
  116. Hsa-miR-223-3p participates in the process of anthracycline-induced cardiomyocyte damage by regulating NFIA gene
  117. Anti-inflammatory effect of ApoE23 on Salmonella typhimurium-induced sepsis in mice
  118. Analysis of somatic mutations and key driving factors of cervical cancer progression
  119. Hsa_circ_0028007 regulates the progression of nasopharyngeal carcinoma through the miR-1179/SQLE axis
  120. Variations in sexual function after laparoendoscopic single-site hysterectomy in women with benign gynecologic diseases
  121. Effects of pharmacological delay with roxadustat on multi-territory perforator flap survival in rats
  122. Analysis of heroin effects on calcium channels in rat cardiomyocytes based on transcriptomics and metabolomics
  123. Risk factors of recurrent bacterial vaginosis among women of reproductive age: A cross-sectional study
  124. Alkbh5 plays indispensable roles in maintaining self-renewal of hematopoietic stem cells
  125. Study to compare the effect of casirivimab and imdevimab, remdesivir, and favipiravir on progression and multi-organ function of hospitalized COVID-19 patients
  126. Correlation between microvessel maturity and ISUP grades assessed using contrast-enhanced transrectal ultrasonography in prostate cancer
  127. The protective effect of caffeic acid phenethyl ester in the nephrotoxicity induced by α-cypermethrin
  128. Norepinephrine alleviates cyclosporin A-induced nephrotoxicity by enhancing the expression of SFRP1
  129. Effect of RUNX1/FOXP3 axis on apoptosis of T and B lymphocytes and immunosuppression in sepsis
  130. The function of Foxp1 represses β-adrenergic receptor transcription in the occurrence and development of bladder cancer through STAT3 activity
  131. Risk model and validation of carbapenem-resistant Klebsiella pneumoniae infection in patients with cerebrovascular disease in the ICU
  132. Calycosin protects against chronic prostatitis in rats via inhibition of the p38MAPK/NF-κB pathway
  133. Pan-cancer analysis of the PDE4DIP gene with potential prognostic and immunotherapeutic values in multiple cancers including acute myeloid leukemia
  134. The safety and immunogenicity to inactivated COVID-19 vaccine in patients with hyperlipemia
  135. Circ-UBR4 regulates the proliferation, migration, inflammation, and apoptosis in ox-LDL-induced vascular smooth muscle cells via miR-515-5p/IGF2 axis
  136. Clinical characteristics of current COVID-19 rehabilitation outpatients in China
  137. Luteolin alleviates ulcerative colitis in rats via regulating immune response, oxidative stress, and metabolic profiling
  138. miR-199a-5p inhibits aortic valve calcification by targeting ATF6 and GRP78 in valve interstitial cells
  139. The application of iliac fascia space block combined with esketamine intravenous general anesthesia in PFNA surgery of the elderly: A prospective, single-center, controlled trial
  140. Elevated blood acetoacetate levels reduce major adverse cardiac and cerebrovascular events risk in acute myocardial infarction
  141. The effects of progesterone on the healing of obstetric anal sphincter damage in female rats
  142. Identification of cuproptosis-related genes for predicting the development of prostate cancer
  143. Lumican silencing ameliorates β-glycerophosphate-mediated vascular smooth muscle cell calcification by attenuating the inhibition of APOB on KIF2C activity
  144. Targeting PTBP1 blocks glutamine metabolism to improve the cisplatin sensitivity of hepatocarcinoma cells through modulating the mRNA stability of glutaminase
  145. A single center prospective study: Influences of different hip flexion angles on the measurement of lumbar spine bone mineral density by dual energy X-ray absorptiometry
  146. Clinical analysis of AN69ST membrane continuous venous hemofiltration in the treatment of severe sepsis
  147. Antibiotics therapy combined with probiotics administered intravaginally for the treatment of bacterial vaginosis: A systematic review and meta-analysis
  148. Construction of a ceRNA network to reveal a vascular invasion associated prognostic model in hepatocellular carcinoma
  149. A pan-cancer analysis of STAT3 expression and genetic alterations in human tumors
  150. A prognostic signature based on seven T-cell-related cell clustering genes in bladder urothelial carcinoma
  151. Pepsin concentration in oral lavage fluid of rabbit reflux model constructed by dilating the lower esophageal sphincter
  152. The antihypertensive felodipine shows synergistic activity with immune checkpoint blockade and inhibits tumor growth via NFAT1 in LUSC
  153. Tanshinone IIA attenuates valvular interstitial cells’ calcification induced by oxidized low density lipoprotein via reducing endoplasmic reticulum stress
  154. AS-IV enhances the antitumor effects of propofol in NSCLC cells by inhibiting autophagy
  155. Establishment of two oxaliplatin-resistant gallbladder cancer cell lines and comprehensive analysis of dysregulated genes
  156. Trial protocol: Feasibility of neuromodulation with connectivity-guided intermittent theta-burst stimulation for improving cognition in multiple sclerosis
  157. LncRNA LINC00592 mediates the promoter methylation of WIF1 to promote the development of bladder cancer
  158. Factors associated with gastrointestinal dysmotility in critically ill patients
  159. Mechanisms by which spinal cord stimulation intervenes in atrial fibrillation: The involvement of the endothelin-1 and nerve growth factor/p75NTR pathways
  160. Analysis of two-gene signatures and related drugs in small-cell lung cancer by bioinformatics
  161. Silencing USP19 alleviates cigarette smoke extract-induced mitochondrial dysfunction in BEAS-2B cells by targeting FUNDC1
  162. Menstrual irregularities associated with COVID-19 vaccines among women in Saudi Arabia: A survey during 2022
  163. Ferroptosis involves in Schwann cell death in diabetic peripheral neuropathy
  164. The effect of AQP4 on tau protein aggregation in neurodegeneration and persistent neuroinflammation after cerebral microinfarcts
  165. Activation of UBEC2 by transcription factor MYBL2 affects DNA damage and promotes gastric cancer progression and cisplatin resistance
  166. Analysis of clinical characteristics in proximal and distal reflux monitoring among patients with gastroesophageal reflux disease
  167. Exosomal circ-0020887 and circ-0009590 as novel biomarkers for the diagnosis and prediction of short-term adverse cardiovascular outcomes in STEMI patients
  168. Upregulated microRNA-429 confers endometrial stromal cell dysfunction by targeting HIF1AN and regulating the HIF1A/VEGF pathway
  169. Bibliometrics and knowledge map analysis of ultrasound-guided regional anesthesia
  170. Knockdown of NUPR1 inhibits angiogenesis in lung cancer through IRE1/XBP1 and PERK/eIF2α/ATF4 signaling pathways
  171. D-dimer trends predict COVID-19 patient’s prognosis: A retrospective chart review study
  172. WTAP affects intracranial aneurysm progression by regulating m6A methylation modification
  173. Using of endoscopic polypectomy in patients with diagnosed malignant colorectal polyp – The cross-sectional clinical study
  174. Anti-S100A4 antibody administration alleviates bronchial epithelial–mesenchymal transition in asthmatic mice
  175. Prognostic evaluation of system immune-inflammatory index and prognostic nutritional index in double expressor diffuse large B-cell lymphoma
  176. Prevalence and antibiogram of bacteria causing urinary tract infection among patients with chronic kidney disease
  177. Reactive oxygen species within the vaginal space: An additional promoter of cervical intraepithelial neoplasia and uterine cervical cancer development?
  178. Identification of disulfidptosis-related genes and immune infiltration in lower-grade glioma
  179. A new technique for uterine-preserving pelvic organ prolapse surgery: Laparoscopic rectus abdominis hysteropexy for uterine prolapse by comparing with traditional techniques
  180. Self-isolation of an Italian long-term care facility during COVID-19 pandemic: A comparison study on care-related infectious episodes
  181. A comparative study on the overlapping effects of clinically applicable therapeutic interventions in patients with central nervous system damage
  182. Low intensity extracorporeal shockwave therapy for chronic pelvic pain syndrome: Long-term follow-up
  183. The diagnostic accuracy of touch imprint cytology for sentinel lymph node metastases of breast cancer: An up-to-date meta-analysis of 4,073 patients
  184. Mortality associated with Sjögren’s syndrome in the United States in the 1999–2020 period: A multiple cause-of-death study
  185. CircMMP11 as a prognostic biomarker mediates miR-361-3p/HMGB1 axis to accelerate malignant progression of hepatocellular carcinoma
  186. Analysis of the clinical characteristics and prognosis of adult de novo acute myeloid leukemia (none APL) with PTPN11 mutations
  187. KMT2A maintains stemness of gastric cancer cells through regulating Wnt/β-catenin signaling-activated transcriptional factor KLF11
  188. Evaluation of placental oxygenation by near-infrared spectroscopy in relation to ultrasound maturation grade in physiological term pregnancies
  189. The role of ultrasonographic findings for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2-negative breast cancer
  190. Construction of immunogenic cell death-related molecular subtypes and prognostic signature in colorectal cancer
  191. Long-term prognostic value of high-sensitivity cardiac troponin-I in patients with idiopathic dilated cardiomyopathy
  192. Establishing a novel Fanconi anemia signaling pathway-associated prognostic model and tumor clustering for pediatric acute myeloid leukemia patients
  193. Integrative bioinformatics analysis reveals STAT2 as a novel biomarker of inflammation-related cardiac dysfunction in atrial fibrillation
  194. Adipose-derived stem cells repair radiation-induced chronic lung injury via inhibiting TGF-β1/Smad 3 signaling pathway
  195. Real-world practice of idiopathic pulmonary fibrosis: Results from a 2000–2016 cohort
  196. lncRNA LENGA sponges miR-378 to promote myocardial fibrosis in atrial fibrillation
  197. Diagnostic value of urinary Tamm-Horsfall protein and 24 h urine osmolality for recurrent calcium oxalate stones of the upper urinary tract: Cross-sectional study
  198. The value of color Doppler ultrasonography combined with serum tumor markers in differential diagnosis of gastric stromal tumor and gastric cancer
  199. The spike protein of SARS-CoV-2 induces inflammation and EMT of lung epithelial cells and fibroblasts through the upregulation of GADD45A
  200. Mycophenolate mofetil versus cyclophosphamide plus in patients with connective tissue disease-associated interstitial lung disease: Efficacy and safety analysis
  201. MiR-1278 targets CALD1 and suppresses the progression of gastric cancer via the MAPK pathway
  202. Metabolomic analysis of serum short-chain fatty acid concentrations in a mouse of MPTP-induced Parkinson’s disease after dietary supplementation with branched-chain amino acids
  203. Cimifugin inhibits adipogenesis and TNF-α-induced insulin resistance in 3T3-L1 cells
  204. Predictors of gastrointestinal complaints in patients on metformin therapy
  205. Prescribing patterns in patients with chronic obstructive pulmonary disease and atrial fibrillation
  206. A retrospective analysis of the effect of latent tuberculosis infection on clinical pregnancy outcomes of in vitro fertilization–fresh embryo transferred in infertile women
  207. Appropriateness and clinical outcomes of short sustained low-efficiency dialysis: A national experience
  208. miR-29 regulates metabolism by inhibiting JNK-1 expression in non-obese patients with type 2 diabetes mellitus and NAFLD
  209. Clinical features and management of lymphoepithelial cyst
  210. Serum VEGF, high-sensitivity CRP, and cystatin-C assist in the diagnosis of type 2 diabetic retinopathy complicated with hyperuricemia
  211. ENPP1 ameliorates vascular calcification via inhibiting the osteogenic transformation of VSMCs and generating PPi
  212. Significance of monitoring the levels of thyroid hormone antibodies and glucose and lipid metabolism antibodies in patients suffer from type 2 diabetes
  213. The causal relationship between immune cells and different kidney diseases: A Mendelian randomization study
  214. Interleukin 33, soluble suppression of tumorigenicity 2, interleukin 27, and galectin 3 as predictors for outcome in patients admitted to intensive care units
  215. Identification of diagnostic immune-related gene biomarkers for predicting heart failure after acute myocardial infarction
  216. Long-term administration of probiotics prevents gastrointestinal mucosal barrier dysfunction in septic mice partly by upregulating the 5-HT degradation pathway
  217. miR-192 inhibits the activation of hepatic stellate cells by targeting Rictor
  218. Diagnostic and prognostic value of MR-pro ADM, procalcitonin, and copeptin in sepsis
  219. Review Articles
  220. Prenatal diagnosis of fetal defects and its implications on the delivery mode
  221. Electromagnetic fields exposure on fetal and childhood abnormalities: Systematic review and meta-analysis
  222. Characteristics of antibiotic resistance mechanisms and genes of Klebsiella pneumoniae
  223. Saddle pulmonary embolism in the setting of COVID-19 infection: A systematic review of case reports and case series
  224. Vitamin C and epigenetics: A short physiological overview
  225. Ebselen: A promising therapy protecting cardiomyocytes from excess iron in iron-overloaded thalassemia patients
  226. Aspirin versus LMWH for VTE prophylaxis after orthopedic surgery
  227. Mechanism of rhubarb in the treatment of hyperlipidemia: A recent review
  228. Surgical management and outcomes of traumatic global brachial plexus injury: A concise review and our center approach
  229. The progress of autoimmune hepatitis research and future challenges
  230. METTL16 in human diseases: What should we do next?
  231. New insights into the prevention of ureteral stents encrustation
  232. VISTA as a prospective immune checkpoint in gynecological malignant tumors: A review of the literature
  233. Case Reports
  234. Mycobacterium xenopi infection of the kidney and lymph nodes: A case report
  235. Genetic mutation of SLC6A20 (c.1072T > C) in a family with nephrolithiasis: A case report
  236. Chronic hepatitis B complicated with secondary hemochromatosis was cured clinically: A case report
  237. Liver abscess complicated with multiple organ invasive infection caused by hematogenous disseminated hypervirulent Klebsiella pneumoniae: A case report
  238. Urokinase-based lock solutions for catheter salvage: A case of an upcoming kidney transplant recipient
  239. Two case reports of maturity-onset diabetes of the young type 3 caused by the hepatocyte nuclear factor 1α gene mutation
  240. Immune checkpoint inhibitor-related pancreatitis: What is known and what is not
  241. Does total hip arthroplasty result in intercostal nerve injury? A case report and literature review
  242. Clinicopathological characteristics and diagnosis of hepatic sinusoidal obstruction syndrome caused by Tusanqi – Case report and literature review
  243. Synchronous triple primary gastrointestinal malignant tumors treated with laparoscopic surgery: A case report
  244. CT-guided percutaneous microwave ablation combined with bone cement injection for the treatment of transverse metastases: A case report
  245. Malignant hyperthermia: Report on a successful rescue of a case with the highest temperature of 44.2°C
  246. Anesthetic management of fetal pulmonary valvuloplasty: A case report
  247. Rapid Communication
  248. Impact of COVID-19 lockdown on glycemic levels during pregnancy: A retrospective analysis
  249. Erratum
  250. Erratum to “Inhibition of miR-21 improves pulmonary vascular responses in bronchopulmonary dysplasia by targeting the DDAH1/ADMA/NO pathway”
  251. Erratum to: “Fer exacerbates renal fibrosis and can be targeted by miR-29c-3p”
  252. Retraction
  253. Retraction of “Study to compare the effect of casirivimab and imdevimab, remdesivir, and favipiravir on progression and multi-organ function of hospitalized COVID-19 patients”
  254. Retraction of “circ_0062491 alleviates periodontitis via the miR-142-5p/IGF1 axis”
  255. Retraction of “miR-223-3p alleviates TGF-β-induced epithelial-mesenchymal transition and extracellular matrix deposition by targeting SP3 in endometrial epithelial cells”
  256. Retraction of “SLCO4A1-AS1 mediates pancreatic cancer development via miR-4673/KIF21B axis”
  257. Retraction of “circRNA_0001679/miR-338-3p/DUSP16 axis aggravates acute lung injury”
  258. Retraction of “lncRNA ACTA2-AS1 inhibits malignant phenotypes of gastric cancer cells”
  259. Special issue Linking Pathobiological Mechanisms to Clinical Application for cardiovascular diseases
  260. Effect of cardiac rehabilitation therapy on depressed patients with cardiac insufficiency after cardiac surgery
  261. Special issue The evolving saga of RNAs from bench to bedside - Part I
  262. FBLIM1 mRNA is a novel prognostic biomarker and is associated with immune infiltrates in glioma
  263. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part III
  264. Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer
Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/med-2023-0660/html
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