TMEM132A: a novel susceptibility gene for lung adenocarcinoma combined with venous thromboembolism identified through comprehensive bioinformatic analysis

Abstract

Background: Mounting evidence indicates that lung adenocarcinoma (LUAD) patients are at elevated risk for venous thromboembolism (VTE), presenting a major clinical challenge. This study utilized public databases to identify crosstalk genes (CGs) between LUAD and VTE, applied machine learning methods to discover shared diagnostic biomarkers, and explored their underlying mechanisms.

Methods: Disease-specific genes for VTE were extracted from comprehensive genomic databases (CTD, DisGeNET, GeneCards, OMIM), while transcriptomic profiles of LUAD and VTE cohorts were retrieved from GEO via GEOquery implementation. Molecular crosstalk analysis identified candidate genes through differential expression algorithms and disease-association metrics. Functional annotation employed GO and KEGG analyses to elucidate the biological significance of identified CGs. LASSO regression analysis of VTE and LUAD matrices yielded overlapping diagnostic biomarkers. Immune contexture was characterized via CIBERSORT deconvolution, followed by correlation analyses between hub genes and immune infiltration profiles. Hub genes expression was corroborated through independent cohort validation and serological quantification. Diagnostic utility was evaluated through receiver operating characteristic (ROC) curve and nomogram. Therapeutic potential was assessed via DSigDB-based drug sensitivity profiling.

Result: Through transcriptomic analysis, we identified 381 CGs, which demonstrated significant enrichment in inflammatory cascades, immunological processes, and coagulation pathways. LASSO regression analysis of LUAD and VTE cohorts revealed TIMP1 and TMEM132A as putative shared diagnostic biomarkers. TMEM132A exhibited significant correlation with immune cell infiltration patterns across both diseases, modulating the immune microenvironment. Validation cohorts and serological assessment confirmed elevated TMEM132A expression in LUAD and LUAD combined with VTE phenotypes. The diagnostic accuracy of TMEM132A was substantiated by ROC curves and nomogram analyses. Pharmacological sensitivity analysis indicated that TMEM132A may serve as a potential target for the therapeutic agents birabresib and abemaciclib.

Conclusion: TMEM132A demonstrates diagnostic utility as a predictive biomarker for VTE occurrence in LUAD, suggesting its potential role as a susceptibility gene in this patient cohort.

Keywords: Lasso algorithm; diagnostic biomarker; immune infiltration; lung adenocarcinoma; venous thromboembolism.

Figure 1

Workflow chart.

Figure 2

Crosstalk genes (CGs) in LUAD and VTE. Volcano plots showed DEGs in (A) GSE10072, (B) GSE32863, (C) GSE40791, (D) GSE43458, (E) GSE46539. (F) Venn plots of the CGs between LUAD genes with VTE genes.

Figure 3

Function enrichment analysis. (A) Biological process of GO. (B) Cellular component of GO. (C) Molecular function of GO. (D) KEGG enrichment analysis of CGs.

Figure 4

Identification of hub genes. (A) LASSO coefficient plots in the GSE75037 dataset. (B) 10-fold cross-validation plots in the GSE75037. (C) LASSO coefficient plots in the GSE48000 dataset. (D) 10-fold cross-validation plots in the GSE48000. (E) LASSO algorithm identified diagnostic characteristic genes related to LUAD. (F) LASSO algorithm identified diagnostic characteristic genes related to VTE. (G) Venn diagram showing hub genes identified in the LASSO model.

Figure 5

The immune infiltration landscape of LUAD and VTE. (A) The proportion of 22 immune cell types in LUAD samples visualized from the stacked column graph. (B) The proportion of 22 immune cell types in VTE samples visualized from the stacked column graph. (C) Immune cell infiltration differences between LUAD and control groups shown in the column scatter plot. (D) Immune cell infiltration differences between VTE and control groups shown in the column scatter plot. (E) Correlation between hub genes and immune cells infiltration levels shown in the correlation scatter plot in LUAD. (F) Correlation between hub genes and immune cells infiltration levels shown in the correlation scatter plot in VTE. *p < 0.05; **p < 0.01; ***p < 0.005; ****p < 0.001; ns, No statistical.

Figure 6

Validation of hub genes in GEO database and ELISA. (A) Sample distribution characteristics of PCA results of GSE75037. (B) Sample distribution characteristics of PCA results of GSE48000. (C) The expression level of hub genes in GSE75037. (D) The expression level of hub genes in GSE48000. (E) Verification of serum TMEM132A expression levels in human samples. **p < 0.01; ***p < 0.005; ****p < 0.001; ns, No statistical.

Figure 7

Evaluation of the diagnostic value of hub genes. (A) ROC curve analysis of TMEM132A in GSE75037. (B) ROC curve analysis of TMEM132A in GSE48000. (C) Nomogram predicting the probability of LUAD. (D) Nomogram predicting the probability of VTE. ***p < 0.005.