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Predictive value of serum magnesium levels for prognosis in patients with non-small cell lung cancer undergoing EGFR-TKI therapy

  • Fang-Zhou Xu , Fu-Rong Meng , Wan-Jing Li , Lu Xu , Hao Zhang , Yan-Bei Zhang and Xiao-Yun Fan EMAIL logo
Published/Copyright: July 24, 2024
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Open Life Sciences
From the journal Open Life Sciences Volume 19 Issue 1

Abstract

The aim of this study is to assess the impact of serum magnesium (Mg) levels on prognostic outcomes in patients with non-small cell lung cancer (NSCLC) undergoing treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). A cohort comprising 91 patients with NSCLC with epidermal growth factor receptor mutations received EGFR-TKI therapy. Assessments of liver and kidney function and electrolyte levels were conducted before treatment initiation and after completing two cycles of EGFR-TKI therapy. Data on variables such as age, gender, presence of distant metastasis, smoking history, other therapeutic interventions, and the specific TKI used were collected for analysis. Cox regression analysis revealed that patients with higher Mg levels prior to EGFR-TKI therapy had significantly longer progression-free survival (PFS) and overall survival (OS). Elevated Mg levels remained predictive of PFS and OS after two cycles of EGFR-TKI therapy. Multiple regression analysis confirmed these findings. Additionally, it was observed that smokers might represent a unique population, demonstrating a correlation between OS and Mg levels. Our findings indicate that serum Mg level is a prognostic factor in patients with NSCLC undergoing EGFR-TKI therapy. This may provide new insights into the underlying mechanisms of EGFR-TKI therapy related to electrolyte balance.

Keywords: EGFR-TKI; magnesium; non-small cell lung cancer; overall survival; progression-free survival

1 Introduction

Non-small-cell lung cancer (NSCLC) originates in the epithelial cells lining the pulmonary airways and includes distinct histological subtypes: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. NSCLC is the most prevalent form of lung cancer, significantly contributing to cancer-associated morbidity and mortality. The introduction of targeted therapeutic interventions and lifestyle modifications has reduced the impact of lung cancer on human health [1]. Targeted and immunotherapy-based treatments have notably improved survival rates for patients with NSCLC [2]. Since 1991, reports from the American Cancer Society and Cancer Statistics Center have shown a steady decline in the cancer mortality rate, with lung cancer experiencing the most pronounced decline among the four leading cancers between 2014 and 2018 [3].

Despite the effectiveness of targeted therapy for NSCLC, drug resistance remains a significant challenge. Throughout the treatment course with first- and second-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI), a substantial proportion of patients experience disease progression due to inadequate therapeutic responses against the T790M mutation and other acquired resistance mechanisms [4]. The expanding array of targeted pharmacotherapeutic agents approved for clinical use has significantly enhanced the management of patients with NSCLC with metastatic EGFR mutations, particularly those harboring the acquired EGFR T790M mutation, who show progression despite ongoing EGFR-TKI treatment [4,5]. Early detection of disease progression and information about drug resistance are crucial for timely drug adjustments and essential for prolonging patient survival and enhancing the quality of life.

The strong association between magnesium (Mg) levels and the severity of various chronic diseases, some of which occur as comorbidities, underscores the importance of Mg levels as a critical observational marker. Reduced serum Mg concentrations have been linked to elevated risks of prediabetes and diabetes [6]. Lötscher et al. demonstrated that Mg enhances T cell immunity and that low serum Mg concentrations are associated with poorer outcomes in cancer immunotherapy, including chimeric antigen receptor T cell therapy [7]. Frequent hypomagnesemia is a significant predictor of decreased survival in patients with head and neck cancer undergoing chemoradiotherapy [8]. However, the relationship between hypomagnesemia and the prognosis of EGFR-TKI therapy for NSCLC has been infrequently studied. We hypothesize that Mg levels correlate with prognosis following EGFR-TKI therapy.

2 Method

2.1 Patient enrollment

From March 2016 to October 2021, 91 patients with NSCLC and EGFR mutations received EGFR-TKI therapy at the First Affiliated Hospital of Anhui Medical University. The inclusion criteria for this study were as follows: (i) a confirmed diagnosis of NSCLC with a clear pathological type; (ii) next-generation sequencing to identify specific genetic mutations; and (iii) completion of more than two cycles of oral EGFR-TKI therapy, with the first effective evaluation conducted after 60 days of treatment. The exclusion criteria were as follows: (i) patients with histologically mixed small cell carcinoma or small cell carcinoma; (ii) patients who were lost to follow-up or deceased within two months of treatment initiation; and (iii) patients with an active infection or kidney disease, as these conditions could affect hematology laboratory marker values.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the Ethics Committee of The First Affiliated Hospital of Anhui Medical University (No.Quick-PJ 2023-12-60).

2.2 Data statistics

Overall survival (OS) was the primary endpoint, while progression-free survival (PFS) was the secondary endpoint. OS was measured from the initiation of EGFR-TKI therapy until all-cause mortality or the date of the last follow-up. PFS was defined as the period from the initiation of TKI therapy to disease progression, tumor metastasis, or all-cause death. Evaluations were conducted monthly during the initial two months following the commencement of EGFR TKI therapy, and subsequently, at three-month intervals. Comprehensive assessments were promptly conducted upon the exacerbation or emergence of new symptoms. The amalgamation of clinical data with findings from chest computed tomography (CT) scans was utilized to appraise therapeutic efficacy, in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1).

Variables extracted included age, gender, distant metastasis, smoking history, body mass index (BMI), other treatments (thoracic surgery, chemotherapy, radiofrequency ablation, and radiotherapy), concomitant diseases (stroke, arterial hypertension, diabetes, and cardiopathy), and the choice of TKI. Non-smokers were defined as patients who had never smoked, while smokers were defined as those who had a history of smoking or had not quit smoking at the time of their cancer diagnosis. Additionally, liver and kidney function, as well as electrolyte levels (including Mg, calcium (Ca), phosphorus (P), potassium (K), blood urea nitrogen (BUN), serum creatinine (SCr), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)), was evaluated prior to and after two cycles of TKI treatment.

2.3 Statistical analyses

Continuous variables are expressed as means ± standard deviations. Univariate prognostic variables were screened using Cox regression analysis. The relationship between Mg levels and PFS and OS before and after therapy was evaluated using a multiple logistic regression model with adjustments for confounding factors. Hazard ratios (HR) are reported as relative risks with corresponding 95% confidence intervals (CI) and were derived using Cox regression analysis. Survival curves were plotted using the Kaplan–Meier method and log-rank test. The optimal cutoff Mg level was determined using the surv_cutpoint algorithm of the survival R package [9,10]. Finally, interactions between the Mg level and the other variables were examined. Statistical significance was set at P < 0.05. Statistical analyses were conducted using Empower® (www.empowerstats.com; X&Y Solutions, Inc., Boston, MA, USA), R (http://www.R-project.org), and R software (version 4.2.0) [11].

3 Results

3.1 Patient demographics

This study included 91 NSCLC patients who underwent EGFR-TKI therapy. Comprehensive patient demographic data are presented in Table 1. The median age of the patients was 61.57 years, and 52.75% were male. Among the cohort, 44 patients (48.35%) received chemotherapy prior to oral TKI treatment. Additionally, six patients (6.59%) underwent radiofrequency ablation, two patients (2.20%) underwent thoracotomy, and three patients (3.30%) underwent radiotherapy. Gefitinib was the most frequently prescribed EGFR-TKI, followed by icotinib and erlotinib. Thirty-one patients were diagnosed with arterial hypertension, 22 with diabetes, seven had a history of stroke, and four had a history of coronary heart disease.

Table 1

Demographics characteristics of 91 patients with NSCLC

Characteristics Mean ± SD Characteristics Mean ± SD
Age 61.57 ± 10.64 Coronary heart disease
ALT(U/L) 38.19 ± 156.69 No 87 (95.60%)
AST(U/L) 28.21 ± 75.12 Yes 4 (4.40%)
BUN (mmol/L) 5.27 ± 2.34 Stroke 　
SCr (μmol/L) 66.65 ± 14.69 No 84 (92.31%)
K (mmol/L) 4.00 ± 0.35 Yes 7 (7.69%)
Ca (mmol/L) 2.26 ± 0.13 Chemotherapy
P (mmol/L) 1.12 ± 0.19 No 47 (51.65%)
Mg (mmol/L) 0.88 ± 0.07 Yes 44 (48.35%)
BMI 22.4 ± 3.4 Radiofrequency ablation 　
Gender 　 No 85 (93.41%)
Female 43 (47.25%) Yes 6 (6.59%)
Male 48 (52.75%) Radiotherapy
Metastasis No 88 (96.70%)
No 7 (7.69%) Yes 3 (3.30%)
Yes 84 (92.31%) Operation 　
Smoke 　 No 89 (97.80%)
No 66 (72.53%) Yes 2 (2.20%)
Yes 25 (27.47%) Drug
Hypertension Gefitinib 42 (46.15%)
No 60 (65.93%) Anlotinib 3 (3.30%)
Yes 31 (34.07%) Icotinib 27 (29.67%)
Diabetes 　 Erlotinib 9 (9.89%)
No 69 (75.82%) Osimertinib 5 (5.49%)
Yes 22 (24.18%) Afatinib 4 (4.40%)
Dacomitinib 1 (1.10%)

ALT, glutamic pyruvic transaminase; AST, glutamic oxaloacetic transaminase; BUN, blood urea nitrogen; SCr, serum creatinine; Ca, calcium; Mg, magnesium; P, phosphorus; K, potassium; BMI, body mass index.

3.2 Prognostic factors before EGFR-TKI therapy

The results of the univariate analysis of liver function, renal function, electrolytes, and medical history prior to therapy are displayed in Table 2. Cox regression analysis revealed that age and gender had little influence on OS and PFS. Renal function and other common electrolytes, such as K, P, and Ca, also had negligible effects on prognosis. ALT and AST had a negligible impact on PFS but had a protective effect on OS. Hypertension, diabetes, history of cardiovascular and cerebrovascular diseases, and history of smoking were risk factors for prognosis after EGFR-TKI therapy, though this trend was not statistically significant. Patients with higher Mg levels before EGFR-TKI therapy had significantly higher PFS and OS (P = 0.0100 and P = 0.0092, respectively). Multiple regression analysis using the Cox survival model confirmed this result (Table 3). After adjusting for variables such as gender, age, SCr, BUN, ALT, AST, disease history, and other treatments, the predictive effect on PFS and OS remained significant (HR = 0.02; 95% CI = 0.00–0.05; P = 0.0216; HR = 0.00; 95% CI = 0.00–0.06; P = 0.0017).

Table 2

Prognostic factors before EGFR-TKI treatment

Statistics PFS HR (95% CI) P value OS HR (95% CI) P value
Gender
Female 43 (47.25%) 1 1
Male 48 (52.75%) 0.85 (0.54, 1.32) 0.4617 0.64 (0.35, 1.18) 0.1519
Age 61.57 ± 10.64 0.99 (0.97, 1.01) 0.2870 1.02 (1.00, 1.05) 0.1032
Metastasis
No 7 (7.69%) 1 1
Yes 84 (92.31%) 1.50 (0.61, 3.74) 0.3784 1.06 (0.33, 3.45) 0.9177
ALT(U/L) 28.21 ± 75.12 1.00 (1.00, 1.00) 0.5770 0.96 (0.92, 1.00) 0.0829
AST(U/L) 38.19 ± 156.69 1.00 (1.00, 1.00) 0.6007 0.98 (0.95, 1.00) 0.0779
BUN (mmol/L) 5.27 ± 2.34 1.03 (0.93, 1.15) 0.5183 1.06 (0.92, 1.22) 0.4023
SCr (μmol/L) 66.65 ± 14.69 1.00 (0.98, 1.02) 0.8521 1.01 (0.99, 1.03) 0.4963
K (mmol/L) 4.00 ± 0.35 1.09 (0.58, 2.06) 0.7841 1.14 (0.45, 2.87) 0.7774
Ca (mmol/L) 2.26 ± 0.13 0.56 (0.09, 3.33) 0.5232 0.39 (0.04, 3.80) 0.4177
P (mmol/L) 1.12 ± 0.19 0.83 (0.25, 2.75) 0.7608 0.77 (0.16, 3.79) 0.7520
Mg (mmol/L) 0.88 ± 0.07 0.02 (0.00, 0.37) 0.0100 0.00 (0.00, 0.26) 0.0092
BMI 22.4 ± 3.4 1.04 (0.97, 1.11) 0.2355 1.03 (0.94, 1.13) 0.5683
Smoke
No 66 (72.53%) 1 1
Yes 25 (27.47%) 1.20 (0.73, 1.97) 0.4786 1.44 (0.75, 2.77) 0.2751
Diseases history
No 55 (60.44%) 1 1
Yes 36 (39.56%) 1.11 (0.70, 1.74) 0.6645 1.10 (0.60, 2.04) 0.7564
Other treatment
No 43 (47.25%) 1 1
Yes 48 (52.75%) 1.05 (0.66, 1.65) 0.8504 0.63 (0.34, 1.18) 0.1474
Table 3

Multiple regression equation of magnesium before EGFR-TKI treatment

Adjust I Adjust II
PFS 0.02 (0.00, 0.51) 0.0179 0.02 (0.00, 0.55) 0.0216
OS 0.00 (0.00, 0.08) 0.0023 0.00 (0.00, 0.06) 0.0017

Adjust I model – adjust for gender and age.

Adjust II model – adjust for gender, age, SCR, BUN, ALT, AST, diseases history, and other treatment.

3.3 Survival curves before EGFR-TKI therapy

Using the surv_cutpoint function in the survival R package, we determined the optimal cutoff Mg level for OS, which was 0.87 mmol/L based on the 91 patients in the study cohort. Consequently, the patients were divided into the high and low Mg level groups, comprising 55 (60.44%) and 36 (39.56%) patients, respectively. The median PFS in the high Mg group was 369 days (range: 323–478 days) compared to that of 275 days (range: 223–363 days) in the low Mg group. The survival curve analysis showed that PFS was significantly higher in the high Mg group than in the low Mg group (P = 0.023; Figure 1). Similarly, OS was higher in the high Mg group (mean: 1,026 days; range: 825–NA days) compared to that in the low Mg group (mean: 517 days; range: 450–846 days). Moreover, the difference in OS between the high Mg and low Mg groups was highly statistically significant (P = 0.00089).

Figure 1 Survival curves before EGFR-TKI therapy according to Mg level: (a) PFS before EGFR-TKI treatment; (b) OS before EGFR-TKI treatment.
Figure 1

Survival curves before EGFR-TKI therapy according to Mg level: (a) PFS before EGFR-TKI treatment; (b) OS before EGFR-TKI treatment.

3.4 Prognostic factors after two EGFR-TKI treatment cycles

Given the excellent pre-treatment data, we further evaluated changes in liver and kidney function, electrolytes, and other parameters after two treatment cycles (Table 4). A univariate analysis of liver function, renal function, and electrolytes post-treatment revealed that most data indicating a decreased risk of disease progression was related to electrolyte levels, particularly Ca and Mg. Notably, only Mg remained statistically significant in terms of PFS, with an HR of 0.01 (95% CI: 0.00–0.07; P = 0.0001), whereas the P value associated with calcium was greater than 0.05, indicating a lack of statistical significance.

Table 4

Prognostic factors after two cycles of EGFR-TKI treatment

Statistics PFS HR (95% CI) P value OS HR (95% CI) P value
Gender
Female 43 (47.25%) 1 1
Man 48 (52.75%) 0.85 (0.54, 1.32) 0.4617 0.64 (0.35, 1.18) 0.1519
Age 61.57 ± 10.64 0.99 (0.97, 1.01) 0.2870 1.02 (1.00, 1.05) 0.1032
Metastasis
No 7 (7.69%) 1 1
Yes 84 (92.31%) 1.50 (0.61, 3.74) 0.3784 1.06 (0.33, 3.45) 0.9177
ALT(U/L) 30.04 ± 20.20 1.00 (0.99, 1.01) 0.5812 1.00 (0.98, 1.01) 0.5103
AST(U/L) 27.27 ± 14.01 1.01 (1.00, 1.03) 0.1704 1.01 (0.98, 1.03) 0.6172
BUN (mmol/L) 5.37 ± 1.82 1.00 (0.88, 1.14) 0.9470 1.07 (0.89, 1.27) 0.4864
SCr (μmol/L) 71.18 ± 19.67 1.00 (0.98, 1.01) 0.7131 1.00 (0.98, 1.02) 0.9446
K (mmol/L) 4.05 ± 0.67 0.94 (0.68, 1.30) 0.7101 0.88 (0.51, 1.52) 0.6437
Ca (mmol/L) 2.22 ± 0.13 0.17 (0.03, 1.09) 0.0618 0.01 (0.00, 0.22) 0.0023
P (mmol/L) 1.12 ± 0.20 1.31 (0.42, 4.13) 0.6428 0.79 (0.17, 3.70) 0.7696
Mg (mmol/L) 0.86 ± 0.09 0.01 (0.00, 0.07) 0.0001 0.01 (0.00, 0.42) 0.0154
BMI 22.4 ± 3.4 1.02 (0.96, 1.09) 0.4576 1.00 (0.98, 1.01) 0.5103
Smoke
No 66 (72.53%) 1 1
Yes 25 (27.47%) 1.20 (0.73, 1.97) 0.4786 1.44 (0.75, 2.77) 0.2751
Diseases history
No 55 (60.44%) 1 1
Yes 36 (39.56%) 1.11 (0.70, 1.74) 0.6645 1.10 (0.60, 2.04) 0.7564
Other treatment
No 43 (47.25%) 1 1
Yes 48 (52.75%) 1.05 (0.66, 1.65) 0.8504 0.63 (0.34, 1.18) 0.1474

For OS, changes in electrolyte levels were the most significant, with Mg and Ca being statistically significant factors. High Ca levels were protective for prognosis (HR = 0.01; 95% CI: 0.00–0.22; P = 0.0023). Since Ca values were only significant for OS after therapy, no additional research was conducted on this parameter. Multiple regression analysis using the Cox survival model (Table 5) adjusted for variables such as gender, age, SCr, BUN, ALT, AST, disease history, and other treatments. Changes in Mg levels continued to exhibit a predictive effect on both PFS (HR = 0.00; 95% CI: 0.00–0.06; P < 0.0001) and OS (HR = 0.00; 95% CI: 0.00–0.20; P = 0.0056).

Table 5

Multiple regression equation of Mg after two cycles of EGFR-TKI treatment

Adjust I Adjust II
PFS 0.01 (0.00, 0.09) 0.0002 0.00 (0.00, 0.06) < 0.0001
OS 0.01 (0.00, 0.25) 0.0073 0.00 (0.00, 0.20) 0.0056

Adjust I model: adjust for – gender; age.

Adjust II model: adjust for – gender; age, SCr, BUN, ALT, AST, diseases history and other treatment.

3.5 Survival curves after EGFR-TKI therapy

The surv_cutpoint function within the survival R package was used again to determine the optimal Mg cutoff value for OS, which was 0.73 mmol/L. There were 81 patients (89.01%) in the high Mg group and 10 patients (10.99%) in the low Mg group. The median PFS for patients in the high Mg group was 363 (range: 289–427) days, which was significantly higher than the 204 (range: 136–360) days for patients in the low Mg group. The survival curve revealed that the PFS was significantly prolonged in the high Mg group (P = 0.0012). The survival curve for OS produced identical results. The median survival time in the high Mg group (876 days; range: 734–NA days) was higher than that in the low Mg group (346 days; range: 225–876 days). There were significant differences between groups in terms of OS (P = 0.00091; Figure 2). The survival R package was used to determine the optimal Mg cutoff levels after therapy. These cutoff levels for serum Mg were lower than the normal range. Therefore, we divided the patients into low (group 1) and high (group 4) groups based on their Mg levels (8–10). The Mg levels were lowest in group 1. The univariate Cox regression analysis was repeated, and related variables were adjusted. The results showed that group 1 was significantly different from the other three groups (Table 6). These outcomes suggest that patients with low Mg levels following TKI therapy have a considerably poorer prognosis compared to other patients.

Figure 2 Survival curves after two cycles of EGFR-TKI therapy according to Mg level: (a) PFS before EGFR-TKI therapy; (b) OS before EGFR-TKI therapy.
Figure 2

Survival curves after two cycles of EGFR-TKI therapy according to Mg level: (a) PFS before EGFR-TKI therapy; (b) OS before EGFR-TKI therapy.

Table 6

Association of serum Mg and prognosis by quartiles of serum Mg

Mg(mmol/L) PFS OS
Groups Statistics model I HR (95% CI) P value model II HR (95% CI) P value model I HR (95% CI) P value model II HR (95% CI) P value
Group4 (0.94) 25 (27.47%) 1 1 1 1
Group3 (0.86–0.94) 21 (23.08%) 1.57 (0.80, 3.06) 0.1895 1.69 (0.84, 3.40) 0.1414 1.38 (0.54, 3.53) 0.4992 1.36 (0.50, 3.68) 0.5487
Group2 (0.79–0.86) 25 (27.47%) 1.49 (0.77, 2.89) 0.2390 1.87 (0.93, 3.76) 0.0796 1.16 (0.44, 3.04) 0.7630 1.38 (0.51, 3.72) 0.5294
Group1 (−0.79) 20 (21.98%) 2.95 (1.53, 5.67) 0.0012 3.51 (1.75, 7.03) 0.0004 2.84 (1.22, 6.62) 0.0154 3.25 (1.34, 7.91) 0.0094

Adjust I model: adjust for gender and age.

Adjust II model: adjust for gender, age, SCr, BUN, ALT, AST, diseases history, and other treatment.

3.6 Interaction test

We further investigated the interactions to elucidate the relationship between Mg levels and the prognosis of EGFR-TKI therapy. Table 7 displays the interactions between Mg levels prior to TKI therapy and other variables. The interaction test results for Mg levels post-EGFR-TKI therapy are shown in Table 8. No variables for PFS interacted significantly with Mg levels either before or after therapy. However, significant variables were identified for OS.

Table 7

Interactions between Mg levels before EGFR-TKI treatment and other factors

Variables PFS OS
N HR 95%CI Low 95%CI High P value P (interaction) N HR 95%CI Low 95%CI High P value P (interaction)
Gender 　 　 　 　 　 0.3363 　 　 　 　 　 0.0080**
Female 43 0.0013 0.0000 0.4606 0.0265* 43 0.0000 0.0000 0.0008 0.0001***
Man 48 0.0411 0.0009 1.9443 0.1048 　 48 0.1667 0.0006 48.4894 0.5360 　
Age 0.0838 0.0700
<65 56 0.1049 0.0015 7.1149 0.2946 　 56 0.0235 0.0001 5.6129 0.1794 　
≥65 35 0.0003 0.0000 0.0595 0.0028** 35 0.0000 0.0000 0.0093 0.0015**
Diseases history 　 　 　 0.1132 　 　 　 0.0527
No 55 0.0790 0.0014 4.3862 0.2155 55 0.0453 0.0002 8.5738 0.2474
Yes 36 0.0003 0.0000 0.0739 0.0037** 36 0.0000 0.0000 0.0091 0.0016 **
Smoke 0.9610 0.0173*
No 66 0.0168 0.0005 0.6157 0.0262* 66 0.0621 0.0004 9.4641 0.2785 　
Yes 25 0.0139 0.0000 11.7887 0.2139 25 0.0000 0.0000 0.0018 0.0006***
Other treatment 　 　 　 0.4047 　 　 　 0.6347
No 43 0.0019 0.0000 0.5581 0.0307* 43 0.0003 0.0000 0.7868 0.0434*
Yes 48 0.0361 0.0006 2.0195 0.1057 　 48 0.0030 0.0000 0.5353 0.0281*
BMI 0.8110 0.5213
Low 45 0.0274 0.0001 5.0882 0.1771 　 45 0.0007 0.0000 1.4918 0.0631 　
High 46 0.0122 0.0002 0.6465 0.0296* 46 0.0130 0.0001 1.6263 0.0779 　

*P < 0.05, **P < 0.01, and ***P < 0.001.

Table 8

Interactions between Mg levels after TKI treatment and other factors

Variables PFS OS
N HR 95% CI Low 95% CI High P value P (interaction) N HR 95%CI Low 95%CI High P value P (interaction)
Gender 0.5738 0.1012
Female 43 0.0019 0.0000 0.1430 0.0045** 43 0.0006 0.0000 0.1345 0.0074**
Man 48 0.0092 0.0003 0.3022 0.0085** 48 0.3237 0.0016 64.8369 0.6766
Age 　 　 　 　 　 0.2887 　 　 　 　 　 0.1043
<65 56 0.0160 0.0005 0.5472 0.0218* 56 0.1183 0.0008 17.5948 0.4030
≥65 35 0.0010 0.0000 0.0478 0.0005*** 35 0.0004 0.0000 0.0469 0.0014**
Diseases history 0.2900 0.0672
No 55 0.0160 0.0005 0.5584 0.0225* 55 0.1503 0.0013 17.2631 0.4336 　
Yes 36 0.0009 0.0000 0.0487 0.0006*** 36 0.0001 0.0000 0.0449 0.0026** 36
Smoke 　 　 　 　 　 0.6774 　 　 　 　 　 0.0183*
No 66 0.0033 0.0001 0.0737 0.0003*** 66 0.1332 0.0019 9.3364 0.3525
Yes 25 0.0133 0.0000 4.6354 0.1481 　 25 0.0000 0.0000 0.0084 0.0020**
Other treatment 0.4456 0.7159
No 43 0.0013 0.0000 0.0739 0.0012** 43 0.0138 0.0001 2.5967 0.1089 　
Yes 48 0.0106 0.0003 0.3967 0.0139* 48 0.0035 0.0000 0.6815 0.0355*
BMI 　 　 　 　 　 0.6905 　 　 　 　 　 0.0990
Low 45 0.0029 0.0001 0.1464 0.0035 ** 45 0.0002 0.0000 0.0766 0.0046**
High 46 0.0086 0.0002 0.3369 0.0110* 46 0.1261 0.0012 13.1979 0.3828 　

* P < 0.05, **P < 0.01, and ***P < 0.001.

For pre-treatment Mg levels, the interaction test revealed that, compared to non-smokers, the Mg levels of smokers were more strongly associated with OS as a protective factor (HR < 0.0001 vs HR = 0.0621; P = 0.0173). Similarly, Mg levels were more strongly correlated with the outcomes of oral EGFR-TKI therapy in women than in men (HR < 0.0001 vs HR = 0.1667; P = 0.008).

Post-therapy, Mg levels demonstrated a specific predictive effect for OS only in smokers (HR < 0.0001 vs HR = 0.1332; P = 0.0183), but not for gender. Although there was no statistically significant difference in OS between females and males (HR = 0.0006 vs HR = 0.3237; P = 0.1012), women exhibited unique characteristics.

4 Discussion

The majority of patients eventually develop resistance to EGFR-TKI drugs, leading to disease progression. Consequently, most patients require regular evaluations using CT and nuclear imaging. However, the limitations of imaging techniques in capturing cellular metabolism hinder the timely detection of disease progression and the modification of treatment plans. Therefore, exploring the relationship between tumor-specific or other relevant blood markers and prognosis following EGFR-TKI therapy is rational [12,13].

Compared to other studies, in our study, we examined the data after two treatment cycles. Based on the results, we propose that patients with extremely low Mg levels following TKI therapy have a significantly poorer prognosis compared to other patients. Furthermore, the interaction test revealed that reduced Mg levels are more likely to be a risk factor in patients with a history of smoking. This observation aligns with previous research, which identified hypomagnesemia as a prognostic indicator of unfavorable outcomes in chemoradiotherapy for head and neck cancer [8]. Similarly, clinical studies suggest that depleted Mg levels constitute a risk factor for hepatocellular carcinoma among patients with nonalcoholic fatty liver disease [14].

Mg, an abundant bivalent cation in the human body, is primarily stored in skeletal muscle, soft tissue, and bone. Hormones or drugs rarely directly affect Mg levels, which are primarily regulated by intestinal absorption and renal excretion [15]. Mg is absorbed through two channel proteins: transient receptor potential melastatin (TRPM) channel types 6 and 7. TRPM7 is widespread, whereas TRPM6 is the primary absorption channel for Mg and is found primarily in the kidneys, distal small intestine, and colon [16]. The epidermal growth factor (EGF) and its receptor (EGFR) regulate the activity of the Mg channel TRPM6. Abnormal activation of EGF causes TRPM6 in the cytoplasm of renal cells to move toward the cell membrane, resulting in increased Mg reabsorption [17,18].

Consequently, EFGR inhibitors could theoretically affect magnesium absorption and induce hypomagnesemia. Consistent with this theory, several studies have reported that the treatment of tumors with EGFR monoclonal antibodies induces hypomagnesemia. Patients treated with cetuximab, alone or in combination with other therapies, have been reported to experience hypomagnesemia [19,20,21]. Anti-EGFR antibodies inhibit TRPM6 activity, reduce Mg influx into cells, and inhibit renal Mg reabsorption. Mutations in the TRPM6 gene, which encodes the epithelial Mg channel, result in secondary hypocalcemia; changes in renal function also affect Mg levels [22,23]. Therefore, after adjusting for liver and kidney function, K, Ca, and other electrolytes in the multiple regression analysis, the outcomes of our study remained relatively unchanged. This further demonstrated the predictive nature of Mg in EGFR-TKI treatment.

So far, no significant clinical trials have demonstrated a clear correlation between EGFR-TKI (gefitinib, erlotinib, or lapatinib) and hypomagnesemia [24,25]. Unlike anti-EGFR monoclonal antibodies, the precise effect of EGFR-TKI on hypomagnesemia remains unclear. Dimke et al. demonstrated that erlotinib can alter EGF-stimulated TRPM6 channel activity at the cellular level. However, animal studies indicate that normal therapeutic doses of erlotinib have no significant effect on serum Mg levels in humans [26].

Mg is a crucial element required to form the active structures of protein kinases, including receptor tyrosine kinases [27]. Cellular studies indicate that high Mg levels can increase tyrosine kinase activity, as both enzyme activity and phosphate transfer require Mg molecules. The Mg-adenosine triphosphate complex binds protein substrates and catalyzes energy for signaling pathway transmission [28,29,30]. Thus, it can be inferred that Mg levels promote EGFR-related pathways. Thus, it can be inferred that Mg levels promote EGFR-related pathways. We hypothesize that patients with elevated serum Mg levels who are treated with EGFR-TKI may have longer PFS and OS based on the following: EGFR mutations lead to increased Mg absorption; that is, elevated Mg levels indicate that EGFR mutations account for a higher proportion of tumor development, thereby increasing the sensitivity of EGFR-TKI treatment. Further research is required to confirm these hypotheses.

Interaction tests demonstrated that gender and smoking factors interacted with Mg levels, while smoking alone interacted with Mg levels both before and after therapy. In a large clinical study of esophageal cancer, the interaction between Mg intake and smoking was statistically significant [31]. However, this study showed a strong positive correlation between Mg and the incidence of esophageal adenocarcinoma among non-smokers than among smokers. In our study, smokers also represented a special group. In patients with prognostic risk factors, such as those over 65 years of age, those with a history of hypertension and other diseases, and those with a low BMI, Mg tended to be a protective factor for OS, although this was not statistically significant.

Chemotherapy utilizing platinum-based agents can impair renal function and result in hypomagnesemia, while anti-EGFR monoclonal antibody therapy can affect Mg absorption. Furthermore, several studies have demonstrated that magnesium supplementation prior to or during treatment enhances patient outcomes [32,33,34]. A study by Minzi et al. revealed that supplementing electrolytes can reduce the risk of nephrotoxicity caused by chemotherapy with cisplatin [32]. Therefore, in combination with our study results, we hypothesize that exogenous electrolyte supplementation during EGFR-TKI therapy may affect therapeutic efficacy, particularly in patients with a history of smoking and other risk factors. However, no studies have reported this previously; therefore, additional clinical trials are required to verify these results.

Our research had several limitations. First, it was a retrospective study with a limited sample size; therefore, bias was inevitable. Second, we did not investigate the effect of electrolyte supplementation on prognosis during therapy. Third, additional follow-up is required to determine the impact of a steady increase in Mg levels on treatment outcomes.

5 Conclusion

Our findings indicate that maintaining a high Mg level is a significant predictor of PFS and OS in patients with NSCLC undergoing EGFR-TKI therapy. Smokers may represent a unique population demonstrating a significant relationship between OS and Mg levels. These results provide new insight into the underlying mechanisms of EGFR-TKI therapy associated with electrolyte balance.

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Acknowledgments

We would like to acknowledge the hard and dedicated work of all the staff who implemented the intervention and evaluation components of the study.

  1. Funding information: This work was funded by the Colleges and Universities in Anhui Province Natural Science Research Projects (No. KJ2018A0208), the Natural Science Foundation project of Anhui Province (2308085MH237, 1708085MH178), Colleges and Universities Provincial Natural Science Research Project of Anhui (2023AH053315), Research Fund of Anhui Institute of translational medicine (2022zhyx-C22), and the Co-construction Project of Clinical and Preliminary Disciplines of Anhui Medical University (2020lcxk003, 2021lcxk005).

  2. Author contributions: Conception and design of the research: Fang-Zhou Xu, Fu-Rong Meng; acquisition of data: Lu Xu, Fang-Zhou Xu; analysis and interpretation of the data: Wan-Jing Li, Lu Xu; statistical analysis: Hao Zhang, Wan-Jing Li; obtaining financing: Yan-Bei Zhang, Xiao-Yun Fan; writing of the manuscript: Fang-Zhou Xu, Fu-Rong Meng, Hao Zhang; critical revision of the manuscript for intellectual content: Yan-Bei Zhang, Xiao-Yun Fan. All authors read and approved the final draft.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2024-02-25
Revised: 2024-05-29
Accepted: 2024-06-17
Published Online: 2024-07-24

© 2024 the author(s), published by De Gruyter

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

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  173. Transcriptomic analysis of liver immune response in Chinese spiny frog (Quasipaa spinosa) infected with Proteus mirabilis
  174. Disruption of BCAA degradation is a critical characteristic of diabetic cardiomyopathy revealed by integrated transcriptome and metabolome analysis
  175. Plant Sciences
  176. Effect of long-term in-row branch covering on soil microorganisms in pear orchards
  177. Photosynthetic physiological characteristics, growth performance, and element concentrations reveal the calcicole–calcifuge behaviors of three Camellia species
  178. Transcriptome analysis reveals the mechanism of NaHCO3 promoting tobacco leaf maturation
  179. Bioinformatics, expression analysis, and functional verification of allene oxide synthase gene HvnAOS1 and HvnAOS2 in qingke
  180. Water, nitrogen, and phosphorus coupling improves gray jujube fruit quality and yield
  181. Improving grape fruit quality through soil conditioner: Insights from RNA-seq analysis of Cabernet Sauvignon roots
  182. Role of Embinin in the reabsorption of nucleus pulposus in lumbar disc herniation: Promotion of nucleus pulposus neovascularization and apoptosis of nucleus pulposus cells
  183. Revealing the effects of amino acid, organic acid, and phytohormones on the germination of tomato seeds under salinity stress
  184. Combined effects of nitrogen fertilizer and biochar on the growth, yield, and quality of pepper
  185. Comprehensive phytochemical and toxicological analysis of Chenopodium ambrosioides (L.) fractions
  186. Impact of “3414” fertilization on the yield and quality of greenhouse tomatoes
  187. Exploring the coupling mode of water and fertilizer for improving growth, fruit quality, and yield of the pear in the arid region
  188. Metagenomic analysis of endophytic bacteria in seed potato (Solanum tuberosum)
  189. Antibacterial, antifungal, and phytochemical properties of Salsola kali ethanolic extract
  190. Exploring the hepatoprotective properties of citronellol: In vitro and in silico studies on ethanol-induced damage in HepG2 cells
  191. Enhanced osmotic dehydration of watermelon rind using honey–sucrose solutions: A study on pre-treatment efficacy and mass transfer kinetics
  192. Effects of exogenous 2,4-epibrassinolide on photosynthetic traits of 53 cowpea varieties under NaCl stress
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  194. An optimization method for measuring the stomata in cassava (Manihot esculenta Crantz) under multiple abiotic stresses
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  199. New advances in protein engineering for industrial applications: Key takeaways
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  201. Research progress of nanoparticles in diagnosis and treatment of hepatocellular carcinoma
  202. Bioelectrochemical biosensors for water quality assessment and wastewater monitoring
  203. PEI/MMNs@LNA-542 nanoparticles alleviate ICU-acquired weakness through targeted autophagy inhibition and mitochondrial protection
  204. Unleashing of cytotoxic effects of thymoquinone-bovine serum albumin nanoparticles on A549 lung cancer cells
  205. Erratum
  206. Erratum to “Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM”
  207. Erratum to “Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation”
  208. Retraction
  209. Retraction to “MiR-223-3p regulates cell viability, migration, invasion, and apoptosis of non-small cell lung cancer cells by targeting RHOB”
  210. Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis”
  211. Special Issue on Advances in Neurodegenerative Disease Research and Treatment
  212. Transplantation of human neural stem cell prevents symptomatic motor behavior disability in a rat model of Parkinson’s disease
  213. Special Issue on Multi-omics
  214. Inflammasome complex genes with clinical relevance suggest potential as therapeutic targets for anti-tumor drugs in clear cell renal cell carcinoma
  215. Gastroesophageal varices in primary biliary cholangitis with anti-centromere antibody positivity: Early onset?
https://doi.org/10.1515/biol-2022-0923
Keywords for this article
EGFR-TKI; magnesium; non-small cell lung cancer; overall survival; progression-free survival
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  194. An optimization method for measuring the stomata in cassava (Manihot esculenta Crantz) under multiple abiotic stresses
  195. Fosinopril inhibits Ang II-induced VSMC proliferation, phenotype transformation, migration, and oxidative stress through the TGF-β1/Smad signaling pathway
  196. Antioxidant and antimicrobial activities of Salsola imbricata methanolic extract and its phytochemical characterization
  197. Bioengineering and Biotechnology
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  199. New advances in protein engineering for industrial applications: Key takeaways
  200. An overview of the production and use of Bacillus thuringiensis toxin
  201. Research progress of nanoparticles in diagnosis and treatment of hepatocellular carcinoma
  202. Bioelectrochemical biosensors for water quality assessment and wastewater monitoring
  203. PEI/MMNs@LNA-542 nanoparticles alleviate ICU-acquired weakness through targeted autophagy inhibition and mitochondrial protection
  204. Unleashing of cytotoxic effects of thymoquinone-bovine serum albumin nanoparticles on A549 lung cancer cells
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  206. Erratum to “Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM”
  207. Erratum to “Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation”
  208. Retraction
  209. Retraction to “MiR-223-3p regulates cell viability, migration, invasion, and apoptosis of non-small cell lung cancer cells by targeting RHOB”
  210. Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis”
  211. Special Issue on Advances in Neurodegenerative Disease Research and Treatment
  212. Transplantation of human neural stem cell prevents symptomatic motor behavior disability in a rat model of Parkinson’s disease
  213. Special Issue on Multi-omics
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