Differences between lung adenocarcinoma and lung squamous cell carcinoma: Driver genes, therapeutic targets, and clinical efficacy

Molecular signatures bidirectionally link myocardial infarction and lung cancer

Myocardial Infarction (MI) and lung cancers are major contributors to mortality worldwide. While seemingly diverse, the two share common risk factors, such as smoking and hypertension. There is a pressing need to identify bidirectional molecular signatures that link MI and lung cancer, in order to improve clinical outcomes for patients. In this study, we identified common differentially expressed genes between MI and lung cancer. Specifically, we identified 1,496 upregulated and 1,482 downregulated genes in the MI datasets. By focusing on the 1,000 most upregulated and downregulated genes in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), we identified 35 genes that are common across MI, LUAD, and LUSC. Functional enrichment analysis revealed shared biological processes, such as "inflammatory response" and "cell differentiation." The Cox proportional hazards model demonstrated a significant association between the shared genes and overall survival in lung cancer patients, as well as with smoking history in these patients. In addition, a machine learning model based on the expression of the shared genes distinguished MI patients from controls, achieving an AUROC of 0.72 and an AUPRC of 0.86. Finally, based on drug repurposing analysis, we proposed FDA-approved drugs potentially targeting the upregulated genes as novel therapeutic options for the co-occurring conditions of MI and lung cancer. Overall, our findings highlight the similarities in molecular makeup between lung cancer and MI.

A method for the identification of lactate metabolism-related prognostic biomarkers and its validations in non-small cell lung cancer

Lactate metabolism (LM) plays a crucial role in tumor progression and therapy resistance in non-small cell lung cancer (NSCLC). Several methods had been developed for NSCLC prognosis prediction based on lactate metabolism-related information. The existing methods for the construction of prognosis prediction models are mostly based on single models such as linear models, SVM, and decision trees. Prognosis biomarkers and prognosis prediction models based on this kind of methods often have limited prognostic performance. In this study, we proposed a novel methodology for constructing prognosis prediction model and identifying lactate-related prognostic biomarkers in NSCLC. We first screened for lactate metabolism-related malignant genes from the scRNA-Seq data of NSCLC malignant cells. We proposed a Cox elastic-net regression combined with genetic algorithm (GA-EnCox) to predict prognosis and optimize the selection of key biomarkers. We identified five key LM-related genes (LYPD3, KRT8, CCT6A, PSMB7, and HMGA1) that significantly correlated with patient prognosis in LUAD cohorts. The prognostic model constructed with these genes outperformed other currently popular models across multiple datasets, demonstrating stable predictive capability. Survival analysis based on bulk RNA-Seq data demonstrated that the low-risk group had significantly better overall survival compared to the high-risk group. Further analysis revealed that lactate metabolism-related prognosis risk might be associated with monocyte lineages such as macrophages and DC's infiltration and these prognosis biomarkers may indicate the therapeutic responses of immune checkpoint inhibitors for NSCLC patients. More importantly, we validated HMGA1 and KRT8 at protein level and their association with histologic grades, stages, and clinical outcomes in consistently treated in-house NSCLC cohorts. Finally, we experimentally validated one of the biomarkers, HMGA1, confirming its role in promoting malignant phenotypes of NSCLC. This study provides valuable insights into the role of lactate metabolism-related biomarkers and their impact on patient outcomes, it was expected to provide important reference value for prognosis assessment and personalized treatment decision of NSCLC patients.

PIK3CA Mutations and Co-Mutations in Operated Non-Small Cell Lung Carcinoma

Background: Understanding PIK3CA mutations and co-mutations in non-small cell lung carcinoma (NSCLC) is critical to developing personalized treatment strategies. Therefore, this study aims to investigate PIK3CA mutations and the accompanying somatic variations in NSCLC. Methods: This retrospective study included 98 patients over 18 years of age who were diagnosed with NSCLC, operated on, and referred to the Molecular Pathology Laboratory between January 2019 and June 2024 for next-generation sequencing panel tests and ALK-ROS1 FISH analysis. Results: All patients were found to carry PIK3CA mutations. Among the 98 NSCLC patients analyzed, 16 (16.33%) were female and 82 (83.67%) were male. The average age of the patients was 64.53 ± 9.63 years, with an age range of 38-84 years, and the majority were 50 years or older. Of the cases, 51 presented the adenocarcinoma subtype, while the remaining 47 showed the squamous cell carcinoma subtype. A smoking history was present in 77 (78.57%) patients, while 21 (21.43%) had no smoking history. The most frequently detected pathogenic or likely pathogenic PIK3CA variations were c.1633G>A p.E545K (32.65%), c.1624G>A p.E542K (11.22%), c.3140A>G p.H1047R (11.22%), c.3140A>T p.H1047L (5.10%), c.1357G>C p.E453Q (4.08%), and c.3143A>G p.H1048R (2.04%). The top 10 mutations that most commonly accompanied PIK3CA variations were KRAS, NF1, TP53, EGFR, PTEN, BRAF, KIT, CDKN2A, SMARCA4, and ATM mutations, respectively. Conclusions:PIK3CA variations, along with other gene variations, may influence cancer progression and thus may play a crucial role in the determination of targeted treatment strategies.