GOLM1 predicts poor prognosis of patients with NSCLC and is associated with the proliferation and chemo-sensitivity of cisplatin in NSCLC cells: bioinformatics analysis and laboratory validation

Integrin α2 promotes immune escape in non-small-cell lung cancer by enhancing PD-L1 expression in exosomes to inhibit CD8 + T-cell activity

This study intended to delineate the mechanism and functional role of integrin α2 (ITGA2) in non-small-cell lung cancer (NSCLC) cell immune escape. Bioinformatics analysis was utilized to analyze ITGA2 expression in NSCLC tissues, and correlations between ITGA2 expression and patient survival time, ITGA2 expression and programmed cell death ligand 1 (PD-L1; CD274) expression, and ITGA2 expression and CD8+ T-cell infiltration. Quantitative real-time polymerase chain reaction detected ITGA2 expression. Transmission electron microscopy was applied to examine the morphology of exosomes, and western blot measured CD9, CD63, and PD-L1 levels. CCK-8 measured cell viability. Cell toxicity experiment measured the killing effect of CD8+ T cells on cancer cells. Enzyme-linked immunosorbent assay assessed secretion levels of interleukin-2, interferon-gamma, tumor necrosis factor-alpha, and PD-L1 expression in exosomes. Immunohistochemistry detected ITGA2, CD8, and PD-L1 expression in patient tissue samples. ITGA2 was highly expressed in NSCLC, and Pearson correlation analysis showed a negative correlation of ITGA2 with CD8+ T-cell infiltration and a positive correlation of ITGA2 with PD-L1 expression. Cell experiments showed that silencing ITGA2 hindered NSCLC cell progression and increased levels of CD8+ T-cell secretory factors. Further mechanism studies found that ITGA2 reduced CD8+ T-cell-mediated antitumor immunity via the increase in PD-L1 expression. Clinical sample testing unveiled that ITGA2 was upregulated in NSCLC tissues. PD-L1 upregulation was seen in exosomes separated from patient blood, and correlation analysis showed a positive correlation of exosomal PD-L1 expression in blood with ITGA2 expression in tissues. This study displays a novel mechanism and role of ITGA2 in NSCLC immune escape, providing directions for the clinical therapy of NSCLC patients.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Data to Establish and Validate a Prognostic Model for Patients With Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data.

Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated.

Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them.

Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.