Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma

The role of B cell immunity in lung adenocarcinoma

Lung cancer is the deadliest cancer globally. Non-small cell lung cancer (NSCLC), including adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, constitutes a significant portion of cases. Adenocarcinoma, the most prevalent type, has seen a rising incidence. Immune checkpoint inhibitors (ICIs) have improved outcomes in lung adenocarcinoma (LUAD), yet response rates remain unsatisfactory. PD-1/PD-L1 inhibitors are primary ICIs for LUAD, targeting the PD-1/PD-L1 pathway between CD8+ T cells and tumor cells. However, LUAD presents a "cold tumor" phenotype with fewer CD8+ T cells and lower PD-1 expression, leading to resistance to ICIs. Thus, understanding the function of other immune cell in tumor microenvironment is crucial for developing novel immunotherapies for LUAD. B cells, which is part of the adaptive immune system, have gained attention for its role in cancer immunology. While research on B cells lags behind T cells, recent studies reveal their close correlation with prognosis and immunotherapy effectiveness in various solid tumors, including lung cancer. B cells show higher abundance, activity, and prognostic significance in LUAD than that in LUSC. This review summarizes the difference of B cell immunity between LUAD and other lung cancers, outlines the role of B cell immunity in LUAD.

A novel mitochondrial quality regulation gene signature for anticipating prognosis, TME, and therapeutic response in LUAD by multi-omics analysis and experimental verification

BACKGROUND

Lung adenocarcinoma (LUAD) is the predominant form of non-small cell lung cancer (NSCLC). Mitochondrial quality-related genes (MQRGs) contribute to the genesis and advancement of tumors. Despite advances in LUAD treatment and detection, early diagnostic biomarkers are still lacking, and the roles of MQRGs in LUAD are not well understood.

METHODS

We extensively examined transcriptome and clinical data from TCGA and GEO databases to discover differentially expressed MQRGs. Utilizing the LASSO algorithm and multivariate COX regression, a predictive risk model was created. Kaplan-Meier study and ROC curves were implemented to predict patient prognosis, resulting in a new Mitochondrial Quality Regulation Gene Signature for accurate prognosis forecasting. R software and packages facilitated statistical, consensus cluster, survival, Cox regression, Lasso regression, and tumor microenvironment analyses. Model-related gene expression was measured using RT-qPCR, immunohistochemistry, single-cell sequencing, HPA data, and UNCAN data.

RESULTS

We created a concise risk model using four MQRGs (STRAP, SHCBP1, PKP2, and CRTAC1) to forecast overall survival in LUAD patients. High-risk patients experienced significantly lower survival rates. Functional analysis linked these MQRGs to alpha-linolenic acid metabolism pathways. Moreover, the tumor immune microenvironment supports previous findings that higher CD8 + T cell infiltration improves LUAD outcomes. Analysis of different risk scores showed increased activated memory T-cell CD4, suggesting its activation is crucial for LUAD prognosis. Nomograms were generated with clinical data and the MQRGscore model. mRNA and IHC analysis manifested significantly upregulated STRAP, SHCBP1, and PKP2 expression and mitigated CRTAC1 expression in the LUAD contrasted with normal lung tissue. qRT-PCR and immunohistochemistry confirmed these findings, aligning with TCGA data.

CONCLUSIONS

We created a succinct MQRGs risk model to ascertain the LUAD patient's prognosis, potentially offering a novel method for diagnosing and treating this condition.

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.