Genetic Variants of CLEC4E and BIRC3 in Damage-Associated Molecular Patterns-Related Pathway Genes Predict Non-Small Cell Lung Cancer Survival

The Role of miR-29a and miR-143 on the Anti-apoptotic MCL-1/cIAP-2 Genes Expression in EGFR Mutated Non-small Cell Lung Carcinoma Patients

The survival rate of lung cancer is low due to the high frequency of drug resistance in patients with mutations in the driver genes. Overexpression of anti-apoptotic genes is one of the most prominent features of tumor drug resistance. EGFR signaling induces the expression of anti-apoptotic genes. Also, microRNAs (miRNAs) have a critical role in regulating biological functions such as apoptosis; a process mostly eluded in cancer progression. The mutation screening was performed on one thousand non-small cell lung carcinoma patients to enroll clinical samples in this study. Bioinformatics analysis predicted that miRNAs (miR-29a, miR-143) might regulate MCL-1 and cIAP-2 expression. We investigated the expression of MCL-1, cIAP-2, miR-29a, and miR-143 encoding genes in adenocarcinoma patients with or without EGFR mutations before treatment. The potential role of miR-29a and miR-143 on gene expression was evaluated by overexpression and luciferase assays in HEK-293T cells. EGFR mutations were found in 262 patients (26.2%) with a greater incidence in females (36.23% vs. 20.37%, P = 0.001). The expression levels of MCL-1 and cIAP-2 genes in patients with mutated EGFR were higher than those of wild-type EGFR. In contrast, compared to those of patients with wild-type EGFR, the expression levels of miR-29a and miR-143 were lower in the patients carrying EGFR mutations. In cell culture, overexpression of miR-29a and miR-143 significantly downregulated the expression of MCL-1 and cIAP-2. Dual-luciferase reporter experiments confirmed that miR-29a and miR-143 target MCL-1 and cIAP-2 mRNAs, respectively. Our results suggest that upregulation of EGFR signaling in lung cancer cells may increase anti-apoptotic MCL-1 and cIAP-2 gene expression, possibly through downregulation of miR-29a-3p and miR-143-3p.

Multi-Algorithm Analysis Reveals Pyroptosis-Linked Genes as Pancreatic Cancer Biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at late stages, limiting treatment options and survival rates. Pyroptosis-related gene signatures hold promise as PDAC prognostic markers, but limited gene pools and small sample sizes hinder their utility. We aimed to enhance PDAC prognosis with a comprehensive multi-algorithm analysis. Using R, we employed natural language processing and latent Dirichlet allocation on PubMed publications to identify pyroptosis-related genes. We collected PDAC transcriptome data (n = 1273) from various databases, conducted a meta-analysis, and performed differential gene expression analysis on tumour and non-cancerous tissues. Cox and LASSO algorithms were used for survival modelling, resulting in a pyroptosis-related gene expression-based prognostic index. Laboratory and external validations were conducted. Bibliometric analysis revealed that pyroptosis publications focus on signalling pathways, disease correlation, and prognosis. We identified 357 pyroptosis-related genes, validating the significance of BHLHE40, IL18, BIRC3, and APOL1. Elevated expression of these genes strongly correlated with poor PDAC prognosis and guided treatment strategies. Our accessible nomogram model aids in PDAC prognosis and treatment decisions. We established an improved gene signature for pyroptosis-related genes, offering a novel model and nomogram for enhanced PDAC prognosis.

Validating a Macrophage Marker Gene Signature (MMGS) in Lung Adenocarcinoma Prognosis and Response to Immunotherapy

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Tumor-associated macrophages play pivotal roles in the tumor microenvironment (TME) and prognosis of LUAD. We first used single-cell RNA sequencing data to identify macrophage marker genes in LUAD. Univariate, least absolute shrinkage and selection operator and stepwise multivariate Cox regression analyses were conducted to evaluate macrophage marker genes as prognostic factors and to construct the macrophage marker genes signature (MMGS). A novel 8-gene signature was constructed to predict prognosis based on 465 macrophage marker genes identified by an analysis of single-cell RNA sequencing data of LUAD, and was also verified in 4 independent GEO cohorts. The MMGS significantly classified patients into high-risk and low-risk groups in terms of OS. A prognostic nomogram based on independent risk factors was established to predict the 2-, 3- and 5-year survival, which indicated superior accuracy in predicting prognosis. The high-risk group was correlated to higher tumor mutational burden, number of neoantigens, T-cell receptor richness, and lower TIDE, which suggested that high-risk patients were more likely to benefit from immunotherapy. The prediction of the possibility of immunotherapy efficacy was also discussed. Analysis of an immunotherapy cohort further verified that patients with high-risk scores had better immunotherapy responses than low-risk patients. The MMGS is a promising signature for predicting prognosis and effectiveness of immunotherapy in patients with LUAD, and may be helpful for clinical decision-making.