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Analysis of somatic mutations and key driving factors of cervical cancer progression

  • Mayinuer Niyazi , Lili Han , Sulaiya Husaiyin , Ayimila Aishanjiang , Min Guo , Gulibanu Muhaimati , Hankez Rozi , Haiyan Sun , Jing Lu , Chunhua Ma , Nuermangul Rouzi , Xiaowan Liu and Kaichun Zhu EMAIL logo
Published/Copyright: July 28, 2023

Abstract

We investigated the somatic mutations and key driving factors of cervical cancer by whole exome sequencing . We found 22,183 somatic single nucleotide variations (SNVs) in 52 paired samples. Somatic SNVs in cervical cancer were significantly higher than those in high-grade intraepithelial lesion and low-grade squamous intraepithelial lesion groups (P < 0.05). C → T/G accounted for 44.12% of base substitution. Copy number variation (false discovery rate < 0.05) was found in 57 chromosome regions. The three regions with significant differences between cervical cancer and non-cervical cancer groups were 1q21.1, 3q26.33, and 13q33.1, covering genes related to tumor proliferation, differentiation, and apoptosis. The frequency of human papillomavirus (HPV) insertion/integration and the number of “tCw” mutations in the cervical cancer group were significantly higher than those in the non-cervical cancer group (P < 0.05). The total number of mutations was positively correlated with the number of “tCw” mutations (R 2 = 0.7967). HPV insertion/integration (OR = 2.302, CI = 1.523–3.589, P = 0.0005), APOBEC enrichment (OR = 17.875, CI = 2.117–150.937, P = 0.001), and HLA-B*39 in HLA-I (OR = 6.435, CI = 0.823–48.919, P = 0.0042) were risk factors for cervical cancer, while HLA-DQB1*05 in HLA-II was a protective factor (OR = 0.426, CI = 0.197–0.910, P = 0.032). Conclusively, HPV insertion/integration, APOBEC mutagenesis, and HLA polymorphisms are high-risk factors for cervical cancer and may be causes of genome instability and somatic mutations. This study provides experimental data for revealing the molecular mechanism of cervical cancer.

1 Introduction

There are nearly 570,000 new cases and about 310,000 deaths of cervical cancer worldwide each year [1,2]. Persistent infection of high-risk human papillomavirus (HPV) is the most important risk factor for cervical cancer [3]. Although most HPVs are cleared by the body, the uncleared HPVs will persist and inactivate the tumor suppressor genes such as TP53 and pRb. Meanwhile, HPVs will also integrate into the host genome, thereby exacerbating genome instability [3,4]. The accumulation of somatic mutations and genome instability caused by persistent HPV infection are all involved in the occurrence of cervical cancer. So far, among thousands of somatic mutations in human cancer types, mutational signatures of more than 40 base substitutions and 10 genome rearrangements have been identified [4,5,6,7]. The public data of next-generation sequencing and TCGA have revealed the complexity and heterogeneity of cervical lesions [8,9]. A large number of studies [10,11,12] have shown that APOBEC3 mutagenesis is a mutational signature found in somatic mutations of a variety of cancers, especially HPV-positive cervical cancer. APOBEC3 mutagenesis is a source of oncogenic driver events and contributes to clonal evolution and intratumor heterogeneity [12,13]. The analysis of the mutation signatures can help decipher the molecular changes and understand the precise molecular phenotype of cervical cancer, thereby contributing to the clinical diagnosis and treatment of cervical cancer [4,14].

With the increased genome data in the TCGA database and the publication of a large number of public pan-cancer studies, mutations that drive the occurrence and development of cervical cancer are constantly being identified and demonstrated. For example, the recurrent mutations of PIK3CA, FBXW7, EP300, MAPK1, HLA-B, NFE2L2, TP53, and ERBB2 in cervical cancer have been confirmed [15,16]. In 2017, TCGA reported the newly identified gene mutations of ERBB3, CASP8, HLA-A, SHKBP, and TGFBR2 in cervical cancer, and ERBB3 (Her3) could be used as a therapeutic target for cervical cancer [12]. In 2019, Huang et al. reported four new significantly mutated genes, including FAT1, MLL3, MLL2, and FADD, in cervical cancer [17]. A deeper understanding of the molecular basis and the development of novel and more effective treatment modalities for cervical cancer remain unmet medical needs.

In this study, we investigated the somatic mutations and key driving factors of cervical cancer by whole exome sequencing (WES). The study subjects with cervical lesions were selected from Xinjiang, China, where there is a high incidence of cervical cancer (459/100,000–590/100,000) [18,19]. The paired cervical lesion tissue/peripheral blood samples were collected for WES. The various somatic mutations of cancer cell exomes, including single nucleotide variation (SNV) analysis, copy number variant (CNV) analysis, HPV insertion/integration analysis, APOBEC mutation mode analysis, and HLA analysis, were evaluated. Our findings may provide a deeper understanding of cervical cancer pathogenesis.

2 Materials and methods

2.1 Subjects

Patients (n = 52) with cervical lesions who visited the Department of Gynecology, Xinjiang Uygur Autonomous Region People’s Hospital from January 2017 to December 2019 were enrolled. All patients were positive for HPV. They were pathologically diagnosed with low-grade squamous intraepithelial lesion (LSIL), high-grade intraepithelial lesion (HSIL), or cervical cancer for the first time. None of these patients received radiotherapy or chemotherapy before sample collection. After HPV typing and pathological diagnosis, the paired fresh-frozen cervical lesion tissues and the paired peripheral blood samples were collected. This study was approved by the Ethics Committee of the People’s Hospital of Xinjiang Uygur Autonomous Region (approval number: KY2017042720), and all methods were also performed following the relevant guidelines and regulations under the committee’s supervision. Moreover, written informed consent was obtained from patients for the collection and use of samples.

2.2 WES

DNA extraction was performed with the magnetic bead method using the MGIEasy Magnetic Beads Genomic DNA Extraction Kit (MGI tech, Shenzhen, China). The Qubit3.0 Fluorometer (ThermoFisher, Q33216) was used for nucleic acid quantification. MGIEasy DNA Library Preparation Kit (MGIEasy, V2.0) was used to construct the library. Agilent 2100 Bioanalyzer (Agilent Technologies, G2939AA) was used to detect the size of DNA fragments, and Qubit3.0 was used to quantify the library. The Agilent SureSelect Human All Exon V7 kit (Agilent Technologies) was used to capture the whole exome region, and then the WES was performed on the MGI 2000 platform (paired ends, 150 bp), with an average sequencing depth of 64×.

2.3 WES data analysis

Burrows-Wheeler Aligner software (BWA, v0.7.17, http://bio-bwa.sourceforge.net/bwa.shtml) was used to map WES reads to the human reference genome hg19 (GRCh37, ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19) for alignment, all of which were combined into a single tandem reference sequence. Using Fastp software (v0.20.0, http://opengene.org/fastp/fastp), the readings with quality Q20 < 90% were deleted; according to the lowest Phred quality score (MapQ), the readings with low mapping quality (MapQ < 5) were deleted. Based on the dbSNP138 database, Mill and 1000G gold standard indels database, and 1000G high confidence SNP database (all the above three databases were downloaded from ftp://ftp.broadinstitute.org/bundle/hg19) and using BaseRecalibrator module in the Genome Analysis Toolkit (GATK, v4.1.8) (https://github.com/broadinstitute/gatk/releases), the SNP correction model was constructed. The base quality in the original sequence was corrected; the systematic error caused by the sequencing instrument was eliminated; and the false positive rate of the mutation site was reduced. Based on the corrected bam file, the Mutect2 module of GATK was first used to analyze the SNP sites of the blood samples. The Genomics DBImport module was used to construct the panel of normals (PON) model of the blood samples, and the Create SomaticPanelOfNormals module was used to filter common germline mutation sites. Then, somatic analysis was performed on paired blood and cervical lesion samples using the Mutect2 module in GATK, the PoN model, and the gnomad database (http://gnomad.broadinstitute.org/downloads). After filtering out the germline mutation of each sample, the raw somatic mutation sites of the tumor samples were obtained. Finally, the FilterMutectCalls module in GATK was used to filter the mutation sites and remove the mutation sites caused by contamination, germline, and artifacts.

2.4 Annotation of SNV and driver gene analysis

ANNOVAR software (v2.1.1, https://annovar.openbioinformatics.org/en/latest/user-guide/download/) was used to annotate the SNVs. The amino acid and protein changes were annotated with the hg19 genome, 1,000 genomes, COSMIC mutation database (cosmic70), Clinvar database (clinvar_20200316), dbSNP150 database, and ExAC exon mutation database.

The driver genes were analyzed using MutSigCV (v1.41, https://software.broadinstitute.org/cancer/cga/sites/default/files/data/tools/mutsig/) based on the oncodriveCLUST algorithm and the oncodrive function (based on the oncodriveFML algorithm) in the R (v4.0.2, https://cran.r-project.org/bin/windows/base/old/4.0.2/) package map tools. The genes with a false discovery rate (FDR) < 0.1 was defined as the mutation driver genes.

2.5 Analysis of CNV

CNVkit software (V0.9.7, https://github.com/etal/cnvkit) was used to analyze the increase or decrease in copy number for a large fragment sequence on the exome. CNV on the tumor exome was conducted on paired blood and cervical lesion samples, using paired blood samples as the control group. Then, the segment module of CNVkit software was used to analyze the absolute copy number of the CNV region. The CNS files of the CNV test results of each group of samples were combined, and then, the GISTIC (V2.0.23, ftp://ftp.broadinstitute.org/pub/GISTIC2.0) software was used to calculate the significantly amplified or missing genomic regions in the cervical cancer group and the non-cervical cancer group (including LSIL and HSIL). The gisticChromPlot module in Maftools was used to integrate the GISTIC results and to plot a distribution map of significant CNVs (FDR < 0.05) in chromosomes. The difference in CNV with FDR < 0.05 between the two sets of samples was analyzed using SPSS, and the distribution of significant CNVs between the two sets of samples was plotted with the gisticOncoPlot module. Then, the richGO packages in the R language were used for GO enrichment analysis on the genes in the significant regions, and the cnetplot function in the enrichplot package was performed to construct a gene distribution map of the significantly enriched pathway.

2.6 HPV insertion/integration analysis

The sequences after fastp filtering and PCR repetition deletion were aligned with the human genome and HPV genome. Genbank (https://www.ncbi.nlm.nih.gov/genbank/) accession number was as follows: HPV16 [NC_001526.4], HPV18 [NC_001357.1], HPV31 [KX638481.1], HPV33 [HQ537707.1], HPV35 [M74117.1], HPV39 [LR862071.1], HPV51 [KT725857.1], HPV52 [HQ537751.1], HPV53 [EF546482.1], and HPV58 [HQ537777.1]. If a chimeric read sequence is aligned with both the HPV genome and the human host genome or one forward/reverse sequencing sequence of the same sequenced fragment is aligned to the human host genome and the other is aligned to the HPV genome, it is considered the HPV integration sequence. When it is a chimeric sequence, it should be aligned to at least 30 nt on the human host or HPV genome; otherwise, the reads were excluded. Subsequently, the BLASTn software (v2.7.1, ftp://ftp.ncbi.nih.gov/blast/executables/LATEST/) was used to further determine the integration sites. The Circos software (v0.69, http://www.circos.ca/software/download/circos/) was used to visualize HPV integration sites in the human genome.

2.7 APOBEC mutagenesis analysis

The mutation mode of APOBEC mutagenesis was defined as C → T or C → G mutation in the tCw motif. The plotApobecDiff function in the R package maftools was used to analyze the number of cytosine mutations in +/−20 nucleotides around the SNV site and to calculate the ratio of cytosine mutations in tCw mode to other cytosine mutations. The mutation site matrix and the APOBEC mutation enrichment score were evaluated with the trinucleotideMatrix function in the R package maftools (v2.2.0). The enrichment of APOBEC-induced mutations in the sample was calculated with the following formula: APOBEC_Enriched = [ntCw*backgroundC]/[nC*backgroundTCW]. According to the score of APOBEC_Enriched > 2, the samples were divided into APOBEC-enriched groups and non-APOBEC-enriched groups. The difference in the mutant genes between the APOBEC-enriched group and the non-APOBEC-enriched group was evaluated with the Fisher test.

2.8 HLA genotyping

The reads of HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 that were aligned to the human genome hg19 and those were not aligned to the hg19 genome were extracted with Samtools (v1.10, http://www.htslib.org/download/). HLA genotyping was performed using HLA-VBSeq software (http://nagasakilab.csml.org/hla/), and the two genotypes with the highest average coverage on each HLA locus were taken as the HLA genotypes of the sample. The HLA allele database of IMGT/HLA database 3.40.0 (ftp://ftp.ebi.ac.uk/pub/databases/ipd/imgt/hla/) was used.

2.9 Statistical analysis

All statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, Illinois, USA). The chi-square test was used for comparing rates between groups. One-way analysis of variance was used for comparing means between samples. Pearson correlation analysis was used for determining correlations between continuous variables. P value < 0.05 was considered statistically significant. The 2 × 2 contingency tables were then used to test for the association of variants and clinical features by odds ratios (ORs) and 95% CIs. The forest plots were plotted using GraphPad Prism 8.0 (GraphPad, San Diego, California, USA).

3 Results

3.1 Basic information of patients

A total of 52 patients were included in this study. Their clinical data were shown in Table 1. They were aged 18–75 years old. There were 5 cases of LSIL, 18 cases of HSIL, and 29 cases of cervical cancer (22 cases of squamous cell carcinoma and 7 cases of adenocarcinoma). All these tumors were primary tumors.

Table 1

Basic information of included patients

Sample number Age (years) Ethnicity Nationality HPV type Pathological type Pathological type
H0360 49 Han China 16 HSIL
H0528 46 Han China 16 LSIL
H0517 52 Han China 16 HSIL
H0439 52 Han China 16 Cervical cancer Squamous cell carcinoma
H0281 46 Han China 16 Cervical cancer Squamous cell carcinoma
H0322 46 Han China 16 Cervical cancer Adenocarcinoma
H0209 62 Han China 52 Cervical cancer Squamous cell carcinoma
H0237 65 Han China 58 Cervical cancer Squamous cell carcinoma
H0421 49 Han China 39 Cervical cancer Squamous cell carcinoma
H0074 49 Han China 16 LSIL
H0076 32 Han China 16 HSIL
H0224 33 Han China 16 HSIL
H0507 50 Han China 16 Cervical cancer Adenocarcinoma
H0284 60 Han China 18 Cervical cancer Adenocarcinoma
H0240 63 Han China 18 Cervical cancer Squamous cell carcinoma
H0042 43 Han China 18 HSIL
H0536 47 Han China 16 HSIL
H0206 37 Han China 16 HSIL
H0584 32 Han China 16, 51 HSIL
H0075 33 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0044 53 Uighur China 58 Cervical cancer Squamous cell carcinoma
H0084 50 Uighur China 53 HSIL
H0083 43 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0402 36 Uighur China 31 HSIL
H0324 25 Uighur China 18 Cervical cancer Squamous cell carcinoma
H0433 60 Uighur China 16 Cervical cancer Adenocarcinoma
H0200 75 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0289 53 Uighur China 18 Cervical cancer Squamous cell carcinoma
H0589 45 Uighur China 16 HSIL
H0604 45 Uighur China 18 Cervical cancer Squamous cell carcinoma
H0030 58 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0455 48 Uighur China 35 HSIL
H0275 55 Uighur China 18 HSIL
H0521 55 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0041 47 Uighur China 18 HSIL
H0212 54 Uighur China 16 HSIL
H0203 68 Uighur China 16 Cervical cancer Adenocarcinoma
H0208 41 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0595 40 Uighur China 18 HSIL
H0047 63 Uighur China 31/51/52 Cervical cancer Squamous cell carcinoma
H0287 56 Uighur China 18 Cervical cancer Squamous cell carcinoma
H0489 44 Uighur China 16 LSIL
H0235 55 Uighur China 16 HSIL
H0067 41 Uighur China 16 HSIL
H0390 33 Uighur China 16 LSIL
H0416 60 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0201 63 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0034 31 Uighur China 16 Cervical cancer Adenocarcinoma
H0320 50 Uighur China 18 Cervical cancer Adenocarcinoma
H0288 63 Uighur China 16 Cervical cancer Squamous cell carcinoma
H0527 54 Uighur China 18 LSIL
H0593 36 Uighur China 33 Cervical cancer Squamous cell carcinoma

Note: HPV, human papillomavirus; LSIL, low-grade squamous intraepithelial lesion; HSIL, high-grade intraepithelial lesion.

3.2 Distribution of somatic SNV in cervical lesions and driver gene analysis

There were at least 35.03 Mbp exons in the 52 pairs of cervical lesion tissue/blood samples. The median coverage depth of paired cervical lesion tissue was 64.84× (range: 24.10–132.32×), and that of paired blood samples was 64.04× (range: 24.39–123.83×) (Table S1). There were 22,183 somatic mutations in 52 paired cervical lesion tissue samples, including 2,004 non-coding regions, 4,772 synonymous mutations, 17 transcription initiation site mutations, 31 splicing site mutations, 565 nonsense mutations, 14 terminator codon mutations, 10,801 missense mutations, 1,524 insert frameshift mutations, 477 codon insertions, 654 missing codons, and 1,324 deletion frameshift mutations (Figure 1(a and b)). Among them, there were five tumor samples with abnormal mutation frequency (each samples > 1,000), and they were classified as “hypermutation” samples (Figure 1(a)). The number of SNV in the cervical cancer group (355.04 ± 42.32) was significantly higher than that in the LSIL group (49.50 ± 24.55) and the HSIL group (76.06 ± 26.00) (P < 0.05) (Figure 1(c)). In all tumor samples, the total mutation density was 12.18 mutations per megabase, and the total mutation density after excluding “hypermutation” samples was 6.35 mutations per megabase (Figure 1(d)). For the distribution of mutations of different bases, the total number of C → T/G mutations accounted for 44.12% of the total mutations (9788/22183) (Figure 1(e)), and the number of C → T/G mutations in the cervical cancer group (182.63 ± 24.66) was significantly higher than the LSIL group (12.00 ± 3.54) and HSIL group (34.38 ± 12.53; P < 0.01) (Figure 1(f)). In the 52 cervical lesion tissue samples, a total of 8,035 genes had mutations, of which 5,325 genes had missense mutations. The top 15 genes included MUC4 (40/52, 46%), MUC16 (19/52, 36%), NBPF1 (18/52, 34%), MUC12 (17/52, 32%), NBPF10 (17/52, 32%), TTN (17/52, 32%), HRNR (16/52, 30%), FLG (15/52, 28%), ANHAK2 (12/52, 23%), MUC6 (12/52, 23%), MUC19 (11/52, 21%), FAT1 (10/52, 19%), IGFN1 (10/52, 19%), OR11H12 (10/52, 19%), and PRSS3 (10/52, 19%) (Figure 1(g)). The frequency of mutations of these 15 genes in the cervical cancer group was significantly higher than that in the HSIL and LSIL groups (P < 0.05) (Figure 1(g)). Driver gene analysis using the oncodrive function in the MutSigCV and maftools package found two newly mutated genes, i.e., OR11H12 (q = 0.00076) and MTCH2 (q = 0.043) (Figure 1(h)).

Figure 1 
                  Analysis of somatic SNV. (a) The number of mutations and mutation types in 52 cervical lesion samples; (b) the total number of various non-synonymous mutation types in 52 cervical lesion samples; (c) comparison of the number of mutations in different groups of samples; (d) Percentage of SNV base changes of each sample; (e) the distribution of base mutations in 52 cervical lesion samples; (f) the number of C → T/G mutations in different groups. Compared with LSIL and HSIL, *P < 0.05, ** P < 0.01; (g) distribution of mutation frequency of genes in descending order. (h) Driver gene analysis using the oncodrive function in the MutSigCV and maftools package found two newly mutated genes.
Figure 1

Analysis of somatic SNV. (a) The number of mutations and mutation types in 52 cervical lesion samples; (b) the total number of various non-synonymous mutation types in 52 cervical lesion samples; (c) comparison of the number of mutations in different groups of samples; (d) Percentage of SNV base changes of each sample; (e) the distribution of base mutations in 52 cervical lesion samples; (f) the number of C → T/G mutations in different groups. Compared with LSIL and HSIL, *P < 0.05, ** P < 0.01; (g) distribution of mutation frequency of genes in descending order. (h) Driver gene analysis using the oncodrive function in the MutSigCV and maftools package found two newly mutated genes.

3.3 Distribution of somatic CNV in cervical lesions

A total of 57 chromosome regions had CNVs (FDR < 0.05), of which there were 35 amplified-type and 22 deleted-type CNVs (Figure 2(a and b)). The chromosome region with the highest incidence of CNVs in both cervical cancer and non-cervical cancer groups was 19q13.2. Among the top 15 chromosome regions with high CNVs frequency, there were three regions with significant differences between the two groups, including 1q21.1 (P = 0.007, OR = 8.724 (1.213–62.722)), 3q26.33 (P = 0.001, OR = 11.103(1.574–78.320)), and 13q33.1 (P = 5.018, OR = 3.172(1.014–9.926)) (Figure 2(c)). The amplified region 1q21.1 was the chr1:146217598–146631220 region. The CNV region was 414 kb in length, and only two genes PRKAB2 and LOC728989 were detected. The amplified region 3q26.33 was chr3:130195727–198022430, and the length of the CNV region was 67 Mb. A total of 425 genes were detected. The deletion region 13q33.1 was chr13:103341378–103419621, with the CNV region of 78 kb in length, and only the CCDC168 gene was detected. Then, we selected three genes with significant differences in the CNV region and analyzed their enrichment at the molecular function, biological process, and cellular component levels (FDR < 0.05, P < 0.05) using enrichGO enrichment analysis (Figure 2(d and e)). The results showed that these genes were closely related to tumor proliferation, differentiation, and apoptosis (such as the P2RY family, PLSCR family, CLDN family, etc.).

Figure 2 
                  Analysis of somatic CNV (a and b): The distribution of CNVs on chromosomes in (a) non-cervical cancer and (b) cervical cancer groups Red represents amplification, blue represents deletion, and the light blue dashed line represents FDR = 0.05. (c) The top 15 chromosome regions with high CNV frequency are shown. The red square represents amplification, and the green square represents deletion. The arrows on the right indicate the three regions with significant differences between the two groups. (d) The gene enrichment at the molecular function, biological process, and cellular component levels (FDR < 0.05, P < 0.05) using enrichGO enrichment analysis. The size of the dots in the figure represents the number of genes in the signal pathway, and the corresponding horizontal axis is the percentage of the number of genes in the total number of genes in the signal pathway. The color represents the significance of the enriched term. (e) Significantly enriched pathways and the genes contained in each pathway. Red font indicates the name of the pathway, different line colors represent different pathways, and the size of the dots represents the number of genes in the pathway.
Figure 2

Analysis of somatic CNV (a and b): The distribution of CNVs on chromosomes in (a) non-cervical cancer and (b) cervical cancer groups Red represents amplification, blue represents deletion, and the light blue dashed line represents FDR = 0.05. (c) The top 15 chromosome regions with high CNV frequency are shown. The red square represents amplification, and the green square represents deletion. The arrows on the right indicate the three regions with significant differences between the two groups. (d) The gene enrichment at the molecular function, biological process, and cellular component levels (FDR < 0.05, P < 0.05) using enrichGO enrichment analysis. The size of the dots in the figure represents the number of genes in the signal pathway, and the corresponding horizontal axis is the percentage of the number of genes in the total number of genes in the signal pathway. The color represents the significance of the enriched term. (e) Significantly enriched pathways and the genes contained in each pathway. Red font indicates the name of the pathway, different line colors represent different pathways, and the size of the dots represents the number of genes in the pathway.

3.4 HPV insertion/integration at the somatic level in cervical lesions

A total of 70 HPV insertion/integration sites were identified in 52 cervical lesion samples, which were discretely distributed on the human exome (Figure 3(a)). The number of HPV insertion/integration sites in the cervical cancer group was 64/70 (91.43%) and in the HSIL group was 6/70 (8.57%). There was no HPV insertion/integration in the LSIL group (0/70, 0.00%). The HPV genome breakage sites were mainly distributed in the E1/E2 region (55.71%, 39/70) and L1 region (15.71%, 11/70) of the HPV genome (Figure 3(b)). There were a few breakage sites in the E5/E6/E7/L2/LCR of the HPV genome. For the integration into the host genome, HPV insertion/integration was not found in LSIL samples (0/5), but it was found in 5.56% (1/18) of HSIL samples and 48.28% (14/29) of cervical cancer samples (Figure 3(c)). Furthermore, it was found that HPV integration was a risk factor for cervical cancer (OR = 2.302, CI = 1.523–3.589, P = 0.0005) (Figure 3(d)). Compared with LSIL/HSIL samples, cervical cancer samples had more insertion sites, and these insertion sites may be related to the occurrence and development of tumors. In addition, the insertion sites in cervical cancer samples may be specifically enriched in certain genes or pathways that are related to the occurrence and development of cancer. It is speculated that the genome instability caused by HPV insertion/integration may be one of the driving factors for the occurrence and development of cervical cancer.

Figure 3 
                  Analysis of HPV insertion/integration in cervical lesions. (a) HPV integration into the host genome. The arrows indicate the HPV integration positions on all exons. (b) The distribution of HPV breakage sites in different regions of the HPV genome. (c) HPV insertion/integration frequency analysis in 52 cervical lesion samples. (d) Relative risk analysis of HPV insertion/integration in cervical cancer.
Figure 3

Analysis of HPV insertion/integration in cervical lesions. (a) HPV integration into the host genome. The arrows indicate the HPV integration positions on all exons. (b) The distribution of HPV breakage sites in different regions of the HPV genome. (c) HPV insertion/integration frequency analysis in 52 cervical lesion samples. (d) Relative risk analysis of HPV insertion/integration in cervical cancer.

3.5 APOBEC-induced mutations in cervical lesions

As shown in Figure 4(a), the number of “tCw” mutations in the cervical cancer group was significantly higher than that in the LSIL and HSIL groups (P = 0.0019). The total number of mutations was positively correlated with the number of “tCw” mutations (R 2 = 0.7967) (Figure 4(b)). Through the analysis with the trinucleotideMatrix function in the R package map tools (v2.2.0), 52 cervical cancer samples were divided into APOBEC-enriched group (14 cases) and non-APOBEC-enriched group (38 cases) (Figure 4(c)). Of note, 13 of the 14 APOBEC-enriched samples were cervical cancer samples. The remaining 1 APOBEC-enriched sample was HSIL. Among all SNVs in the sample, the proportion of “tCw” mutations in the APOBEC-enriched group accounted for 23.82%, which was significantly higher than that in the non-APOBEC-enriched group (5.77%; P < 0.05). Additionally, the APOBEC enrichment was a risk factor for the occurrence of cervical cancer (OR = 17.875, CI = 2.117–150.937, P = 0.001) (Figure 4(d)). The mutation frequency of ten genes in APOBEC-enriched group was significantly higher than that in the non-APOBEC-enriched group, namely TMT2D, C6orf132, KALRN, KLHDC7B, MYH6, DNAH10, DYNC1H1, NOTCH1, PLEC, and FOX2 genes (P < 0.05) (Figure 4(e)). Among them, C6orf132, KALRN, KLHDC7B, MYH6, and FOXF2 only had mutations in the APOBEC-enriched group.

Figure 4 
                  Analysis of APOBEC-induced mutations in cervical lesions. (a) Comparison of the number of “tCw” mutations in different groups. (b) Correlation analysis between the number of “tCw” mutations and the total number of mutations. (c) APOBEC enrichment analysis. (d) Forest plot of APOBEC enrichment and relative risk of cervical cancer. (e) Genes with a significant difference in mutation frequency between the APOBEC-enriched group and non-APOBEC-enriched group. Compared between the APOBEC-enriched group and the non-APOBEC-enriched group, *P < 0.05, **P < 0.01.
Figure 4

Analysis of APOBEC-induced mutations in cervical lesions. (a) Comparison of the number of “tCw” mutations in different groups. (b) Correlation analysis between the number of “tCw” mutations and the total number of mutations. (c) APOBEC enrichment analysis. (d) Forest plot of APOBEC enrichment and relative risk of cervical cancer. (e) Genes with a significant difference in mutation frequency between the APOBEC-enriched group and non-APOBEC-enriched group. Compared between the APOBEC-enriched group and the non-APOBEC-enriched group, *P < 0.05, **P < 0.01.

3.6 Association analysis between HLA class I/II alleles and cervical cancer

The results of HLA class I/II allele typing of 52 patients showed that in HLA class I, a total of 33 genotypes were detected in HLA-A, HLA-B, and HLA-C loci (Figure 5(a–c)). In HLA class II, a total of 38 genotypes were detected in HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 loci (Figure 5(d–h)). In HLA-I, HLA-B*39 was significantly different between the non-cervical cancer group and the cervical cancer group (Figure 5(i)). Moreover, HLA-B*39 was identified as a risk factor for cervical cancer (OR = 6.435, CI = 0.823–48.919, P = 0.0042) (Figure 5(i)). In HLA-II, HLA-DQB1*05 was significantly different between the non-cervical cancer group and the cervical cancer group (Figure 5(i)). Importantly, HLA-DQB1*05 was a protective factor for cervical cancer (OR = 0.426, CI = 0.197–0.910, P = 0.032) (Figure 5(i)).

Figure 5 
                  Analysis of HLA alleles. The distribution of genotypes on different HLA loci between cervical cancer and non-cervical cancer groups. (a) HLA-A genotype distribution; (b) HLA-B genotype distribution; (c) HLA-C genotype distribution; (d) HLA-DPA1 genotype distribution; (e) HLA-DPB1 genotype distribution; (f) HLA-DQA1 genotype distribution; (g) HLA-DQB1 genotype distribution; (h) HLA-DRB1 genotype distribution; (i) forest plot of HLA-B*39 and HLA-DQB1*05 and risk of cervical cancer.
Figure 5

Analysis of HLA alleles. The distribution of genotypes on different HLA loci between cervical cancer and non-cervical cancer groups. (a) HLA-A genotype distribution; (b) HLA-B genotype distribution; (c) HLA-C genotype distribution; (d) HLA-DPA1 genotype distribution; (e) HLA-DPB1 genotype distribution; (f) HLA-DQA1 genotype distribution; (g) HLA-DQB1 genotype distribution; (h) HLA-DRB1 genotype distribution; (i) forest plot of HLA-B*39 and HLA-DQB1*05 and risk of cervical cancer.

4 Discussion

Persistent HPV infection is the main risk factor for the occurrence and development of cervical cancer. Whether the genome instability caused by HPV insertion/integration will lead to the occurrence and accumulation of somatic mutations in cervical cancer has attracted much attention. A comprehensive understanding of the various mutation types of somatic cells, analysis of mutation signatures, and identification of new driver genes have important guiding significance in the diagnosis and treatment of cervical cancer. Some important and common mutations including oncogenes (PIK3CA, EGFR, and KRAS) and suppressor genes (PTEN, TP53 and STK11, and MAPK) have been reported and confirmed in cervical cancers of different populations [20,21,22,23,24]. In 2017, an analysis of the molecular characteristics of the cervical cancer genome found that SHKBP1, ERBB3, CASP8, HLA-A, and TGFBR2 were significant driver gene mutations in cervical cancer and that HLA-B, EP300, and FBXW7 driver gene mutations were newly identified [12]. These mutations may provide novel biomarkers for the early identification of cervical cancer [25,26].

This study performed WES on 52 pairs of cervical lesion tissue/blood paired samples and found that many membrane mucin genes (such as MUC4, 6, 12, 16, 19) had high-frequency mutations, among which MUC4 was mutated in 46.7% of the samples. Similar to our results, Das et al. also found that the MUC family of cervical cancer patients in India had a large number of somatic mutations [27]. Among them, MUC16 carried 11 somatic mutations and had the highest mutation frequency. Meanwhile, MUC17 also had a high frequency of mutations [27]. Liu et al. analyzed the frequency of gene mutations in 31 cancers and found that among the 19 MUC family genes, nine genes were high-frequency mutation genes, of which four (MUC4, MUC5B, MUC16, and MUC17) were common high-frequency mutation genes [28]. MUC4 is a membrane-bound mucin that can promote the progression of carcinogenesis. It has been confirmed that it may be a tumor prognostic biomarker [29]. However, in previous studies [12,24], no significant mutations in this family were reported. We consider that the discrepancy may be related to the differences in the genetic background of study participants. Additionally, this study found two significantly mutated driver genes MTCH2 and OR11H2, the role of which in cervical cancer has not been reported. MTCH2 is a mitochondrial outer membrane protein that regulates mitochondrial metabolism. It is shown that MTCH2 can inhibit tumor invasion in malignant gliomas [30]. However, whether MTCH2 is involved in regulating the occurrence and development of cervical cancer is unclear. OR11H2 is a member of the G protein-coupled receptor family, and there is no report on whether it is related to tumors. We will carry out a follow-up study to verify the roles of MTCH2 and OR11H2 in cervical cancer.

Marchuk et al. [31] performed WES sequencing on 672 tumor-containing samples and found a total of five pathogenic CNVs, namely, 1q21.1 deletion, 7q11.23 duplication, 15q11.2 deletion, 17p12 duplication, and trisomy 21. In a study on cervical cancer [32], 88 paired tumor samples and blood samples were analyzed. A total of 26 amplifications and 37 deletions were detected, including 3q26.31 (TERC, MECOM; 78%), 3q28 (TP63; 77%), and other CNVs. This study identified 57 regions of the chromosomes with CNVs. The frequency of CNVs in 1q21.1, 3q26.33, and 13q33.1 in the cervical cancer group was significantly higher than that in the non-cervical cancer group. GO analysis found that these three regions covered genes related to a variety of signaling pathways closely related to cancer occurrence and cell proliferation, differentiation, and apoptosis (such as the P2RY family, PLSCR family, CLDN family, etc.). The genomic instability caused by CNV in these regions may be a key factor responsible for cervical cancer occurrence. Our results were partially consistent with previous findings [12,31]. The discrepancy may be caused by differences in the genetic background of the study population.

Many studies [32,33] have shown that HPV integration usually disrupts the open reading frames of E1 and E2, and upregulates the expression of E6 and E7 oncogenes. E6 can bind and degrade the tumor suppressor protein P53 and the pro-apoptotic protein BAK, thereby increasing the resistance of host cells to apoptosis and allowing viral DNA replication [34]. Consistently, we found that most HPV insertion/integration sites were located in the E1/E2 region, and the number of HPV insertion/integration in the cervical cancer group was the largest (91.43%).

We also found that 55.59% of cervical lesions were C → T/G mutations, and C → T/G mutations were in line with the “tCw” mutations induced by APOBEC. Analysis of the “tCw” mutations revealed that the number of “tCw” mutations in cervical lesions was positively correlated with the total number of mutations. The number of “tCw” mutations in cervical cancer was significantly higher than that in the HSIL group and the LSIL group. APOBEC enrichment was a high-risk factor for cervical cancer. Similar to our results, there have been studies reporting that HPV sequence integration was accompanied by an increase in the expression of APOBEC3A during the malignant transformation of cervical cancer and significant APOBEC mutagenesis was shown in other HPV-related malignancies [35,36]. A study using TCGA has also shown that the amount of mutations induced by APOBEC in cervical cancer is significantly positively correlated with the total number of mutations, and APOBEC mutagenesis is the main source of mutations in cervical cancer [12].

Under the same HPV exposure conditions, the incidence of cervical cancer is different [37,38], which may be related to the difference in immune response caused by HLA gene polymorphism [39]. Bao et al. found that in the European population, the alleles HLA-DRB5*0101 and HLA-DRB3*9901 were risk factors for cervical cancer, and HLA-DRB3*301 was a protective factor for cervical cancer [40]. However, Chen et al. conducted a GWAS study on the cervical cancer susceptibility loci in European populations and found that HLA-DPB1*04:02 and rs3117027 G alleles were significantly associated with reduced risk of cervical cancer, while HLA-DPB1*03:01 was significantly associated with increased risk of cervical cancer [41,42]. Another GWAS study conducted by Shi et al. in the Chinese population showed that HLA-DPB1*03:01 and DPB1*04:01 were associated with cervical cancer susceptibility, while HLA-DPB1*05:01 and rs4282438 G alleles showed protective effects [43]. However, we obtained inconsistent results. It is reported that different genetic backgrounds may lead to differences in the incidence and gene mutation of cervical cancer [44]. Thus, we consider that this discrepancy may be due to differences in the genetic background of study participants. The samples included in this study were from Uighur and Han populations in Xinjiang, China, which have unique genetic backgrounds.

This study has some limitations. First, the sample size was small. Second, the follow-up information, especially that of LSIL and HIS patients, was incomplete. Due to the small sample size and incomplete patient data, a more detailed analysis of cervical cancer cannot be performed. Third, we did not further analyze the histological subtypes of cervical cancers. Further studies are warranted to verify the results.

In this study, through WES, we analyzed the genetic mutation signatures of cervical lesions at different stages. We found that HPV insertion/integration, APOBEC mutagenesis, and HLA polymorphism were high-risk factors for cervical cancer. In addition, MTCH2 and OR11H2 were identified for the first time as significantly mutated genes in cervical cancer. Our findings may provide an in-depth understanding of the molecular mechanisms of cervical cancer and may provide insights into the development of new diagnostic and therapeutic targets for cervical cancer.


# Equal contributors.

tel: +86-13909923043

Acknowledgements

Thanks to Lu Liu, Wenbo Zhao and Chaoyang Chen of Xinjiang Dingju Biotechnology Co., Ltd. for their technical support in whole-genome sequencing and data analysis.

  1. Funding information: This study is supported by the National Natural Science Foundation of China (grant no. U1603286).

  2. Author contributions: M.N. and K.Z. conceived and designed the experiments. L.L.H., S.H., and M.G. collected the data. K.C.Z., L.L.H., C.H.M., and A.A. analyzed the data. M.N. and K.Z. interpreted the data. M.N., L.L.H., and S.H. prepared the manuscript. G.M., H.R., H.Y.S., J.L., C.H.M., N.R., and X.W.L. searched the literature.

  3. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

  4. Data availability statement: All the sequence data have been deposited in the NCBI database (accession number PRJNA679691) (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA679691).

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Received: 2023-01-11
Revised: 2023-05-29
Accepted: 2023-06-26
Published Online: 2023-07-28

© 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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  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
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