Identification of immunotherapy biomarkers for improving the clinical outcome of homologous recombination deficiency patients with lung adenocarcinoma.

Homologous recombination deficiency (HRD) is a common molecular signature of genomic instability and has been shown to be a biomarker for targeted therapies. However, there is a lack of studies on the role of HRD changes in lung adenocarcinoma (LUAD) transcriptomics. HRD scores were determined using single nucleotide polymorphism (SNP) array data from LUAD patients from The Cancer Genome Atlas (TCGA) database. Transcriptional data from patients with different scores were analyzed to identify biomarkers associated with HRD. Candidate biomarkers were validated using Gene Expression Omnibus (GEO)-sourced datasets and an immunotherapy cohort. According to the bulk transcriptome and clinical characteristics of 912 LUAD patients and Single-cell RNA-seq of 9 LUAD patients from TCGA and GEO databases, we observed increased MS4A6A expression in HRD tumors; high MS4A6A expression predicted improved survival outcomes. Furthermore, a comprehensive analysis of the tumor immune microenvironment (TIME) revealed a positive correlation between MS4A6A expression and neoantigen loading and immune cell infiltration. Additionally, the immunotherapy cohort confirmed the possibility of using MS4A6A as a biomarker. Collectively, we suggest that MS4A6A is associated with HRD and provide a new perspective toward identifying promising biomarkers for immunotherapy.

Identification of Tumor Antigens and Immune Subtypes of Esophageal Squamous Cell Carcinoma for mRNA Vaccine Development.

Purpose: The applicability of mRNA vaccines against esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we identified potential antigens for developing mRNA vaccines against ESCC and characterized immune subtypes to select appropriate patients for vaccination. Methods: RNA-seq, genetic alteration data, and corresponding clinical information of ESCC patients were obtained from the Cancer Genome Atlas (TCGA) database. The RNA-seq data of normal esophageal tissue were obtained from the Genotype-Tissue Expression (GTEx) database. Potential tumor antigens were screened by analyzing differentially expressed and mutated genes and potential antigens with significant differences in prognosis were screened using the Kaplan-Meier method. The proportion of immune cell infiltration in the tumor microenvironment was estimated using CIBERSORT and MCPcounter, and the correlation of potential antigens with antigen-presenting cells and major histocompatibility complex class II was analyzed. Subsequently, immune subtypes were constructed using consensus clustering analysis and characterized by single-sample gene set enrichment analysis and weighted gene co-expression network analysis (WGCNA). The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to analyze the drug sensitivity of different immune subtypes. Results: Four overexpressed and mutated tumor antigens associated with antigen presentation and poor prognosis were identified in ESCC, including NLRC5, FCRL4, TMEM229B, and LCP2. By consensus clustering, we identified two immune-associated ESCC subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2); the prognosis of the two subtypes was statistically different. In addition, the two immune subtypes had distinctly different cellular, molecular, and clinical characteristics. IS1 patients have a distinct immune "hot" phenotype with strong immune tolerance, whereas patients with IS2 have an immune "cold" phenotype. Differential expression of immune checkpoints and immunogenic cell death modulators was observed between the different immune subtypes. Finally, we found that IS1 and IS2 patients showed different drug sensitivities to common anti-tumor drugs, possibly facilitating the development of individualized treatment regimens for patients. Conclusion: NLRC5, LCP2, TMEM229B, and FCRL4 are potential antigens for ESCC mRNA vaccines, and such vaccines may be more suitable for IS2 patients. This study provides a theoretical basis for mRNA vaccines against ESCC, by identifying the critical characteristics to predict ESCC prognosis and select suitable patients for vaccination.

Systematic profiling of ferroptosis gene signatures predicts prognostic factors in esophageal squamous cell carcinoma.

We developed a predictive model associated with ferroptosis to provide a more comprehensive view of esophageal squamous cell carcinoma (ESCC) immunotherapy. Gene expression data and corresponding clinical outcomes were obtained from the GEO and The Cancer Genome Atlas (TCGA) databases, and a ferroptosis-related gene set was obtained from the FerrDb database. We identified 45 ferroptosis-related genes that were differentially expressed, including enrichment in genes involved in the immune system process. We established a ferroptosis-related gene-based prognostic model based on the results of univariate Cox regression and multivariate Cox regression analyses, with an area under the curve (AUC) of 0.76 (3 years). We found that the patients with low-risk scores showed a higher proportion of CD8+ T cells, CD4+ memory activated T cells, etc. Finally, a predictive ferroptosis-related prognostic nomogram, which included the predictive values of age, gender, grade, TNM stage, and risk score, was established to predict overall survival. In sum, we developed a ferroptosis-related gene-based prognostic model that provides novel insights into the prediction of ESCC prognosis and identifies the relevance of the immune microenvironment for patient outcomes.

Construction of a 26­feature gene support vector machine classifier for smoking and non­smoking lung adenocarcinoma sample classification.

The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus database through pre­set criteria and the expression data were processed using meta­analysis. Differentially expressed genes (DEGs) between LAC samples of smokers and non­smokers were identified using limma package in R. The classification accuracy of selected DEGs were visualized using hierarchical clustering analysis in R language. A protein­protein interaction (PPI) network was constructed using gene interaction data from the Human Protein Reference Database for the DEGs. Betweenness centrality was calculated for each node in the network and genes with the greatest BC values were utilized for the construction of the support vector machine (SVM) classifier. The dataset GSE43458 was used as the training dataset for the construction and the other datasets (GSE12667 and GSE10072) were used as the validation datasets. The classification accuracy of the classifier was tested using sensitivity, specificity, positive predictive value, negative predictive value and area under curve parameters with the pROC package in R language. The feature genes in the SVM classifier were subjected to pathway enrichment analysis using Fisher's exact test. A total of 347 genes were identified to be differentially expressed between samples of smokers and non­smokers. The PPI network of DEGs were comprised of 202 nodes and 300 edges. An SVM classifier comprised of 26 feature genes was constructed to distinguish between different LAC samples, with prediction accuracies for the GSE43458, GSE12667 and GSE10072 datasets of 100, 100 and 94.83%, respectively. Furthermore, the 26 feature genes that were significantly enriched in 9 overrepresented biological pathways, including extracellular matrix­receptor interaction, proteoglycans in cancer, cell adhesion molecules, p53 signaling pathway, microRNAs in cancer and apoptosis, were identified to be smoking­related genes in LAC. In conclusion, an SVM classifier with a high prediction accuracy for smoking and non­smoking samples was obtained. The genes in the classifier may likely be the potential feature genes associated with the development of patients with LAC who smoke.

Five miRNAs Considered as Molecular Targets for Predicting Esophageal Cancer.

BACKGROUND: Esophageal cancer (EC) is one of the most aggressive malignant gastrointestinal tumors; however the traditional therapies for EC are not effective enough. Great improvements are needed to explore new and valid treatments for EC. We aimed to screen the differentially expressed miRNAs (DEMs) in esophageal cancer and explore the pathogenesis of esophageal cancer along with functions and pathways of the target genes. MATERIAL AND METHODS: miRNA high-throughput sequencing data were downloaded from The Cancer Genome Atlas (TCGA), then the DEMs underwent principal component analysis (PCA) based on their expression value. Following that, TargetScan software was used to predict the target genes, and enrichment analysis and pathway annotation of these target genes were done by DAVID and KEGG, respectively. Finally, survival analysis between the DEMs and patient survival time was done, and the miRNAs with prediction potential were identified. RESULTS: A total of 140 DEMs were obtained, 113 miRNAs were up-regulated including hsa-mir-153-2, hsa-mir-92a-1 and hsa-mir-182; while 27 miRNAs were down-regulated including hsa-mir comprising 29a, hsa-mir-100 and hsa-mir-139 and so on. Five miRNAs (hsa-mir-103-1, hsa-mir-18a, hsa-mir-324, hsa-mir-369 and hsa-mir-320b-2) with diagnostic and preventive potential were significantly correlated with survival time. CONCLUSIONS: The crucial molecular targets such as p53 may provide great clinical value in treatment, as well to provide new ideas for esophageal cancer therapy. The target genes of miRNA were found to play key roles in protein phosphorylation, and the functions of the target genes during protein phosphorylation should be further studied to explore novel treatment of EC.

Thapsigargin induces apoptosis by impairing cytoskeleton dynamics in human lung adenocarcinoma cells.

The objective of this study was performed to investigate the effects of thapsigargin on apoptosis, actin cytoskeletal dynamics, and actin cytoskeletal proteins in human lung adenocarcinoma cell. Thapsigargin is a specific irreversible inhibitor of ER calcium-ATPase, which may promote ER stress by depletion of lumenal calcium stores and show potential to induce cell death. The effects of thapsigargin on the apoptosis in A549 cells were assayed by Hoechst staining. Moreover, the F-actin staining by Rhodamine-phalloidin and RhoA antibody for cytoskeleton organizations were applied to A549 cells. To confirm the impairment of cytoskeletal dynamics treated with thapsigargin, western blots were applied to analyze the protein levels of p-Cofilin-1 (Ser3), Cofilin-1, and pPaxillin (Tyr118), as well as RhoA and pS6 (S240/244). Results suggest that thapsigargin may induce cell death in A549 cells with a time- and dose-dependent manner. The F-actin fibers and RhoA signals are also reduced with a time- and dose-dependent manner by thapsigargin treatment. The phosphorylation forms of Cofilin-1 and paxillin are attenuated by 1 µM thapsigargin treatment for 24 h. These alternations may be caused by the inhibition of of mTORC1 activities (indicated by pS6 (Ser240/244)) and RhoA pathways after thapsigargin treatment. The present findings highlight important roles of calcium entry in cytoskeleton organization and apoptosis in human lung adenocarcinoma cells and will help to set a stage to the clinical treatment of cancer cell metastasis.

Quantitative assessment of the effects of MMP-2 polymorphisms on lung carcinoma risk.

BACKGROUND: Previous studies assessing associations between matrix metalloproteinase 2 (MMP-2) polymorphisms and lung cancer risk reported conflicting results. A meta-analysis was therefore performed to derive a more precise estimation. METHOD: Case-control studies assessing associations between MMP-2 C735T and C1306T polymorphisms and lung cancer risk were included. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. RESULTS: 7 studies with a total of 3,189 lung cancer cases and 3,013 controls were finally included into this meta-analysis. Overall, the MMP-2 C735T polymorphism was associated with lung cancer risk under the homozygote model (CC versus TT: OR =1.44, 95% CI = 1.03-2.02, I2 = 0%), while the MMP- 2 C1306T polymorphism also associated demonstrated links with all four models (all P values less than 0.05). Subgroup analyses by race suggested obvious associations between MMP-2 C735T and C1306T polymorphisms and lung cancer risk in Asians but not in Caucasians. There was no evidence for publication bias. CONCLUSION: Currently available evidence supports teh conclusion that MMP-2 C735T and C1306T polymorphisms influence susceptibility to lung cancer in Asians.

Down-regulation of ING4 is associated with initiation and progression of lung cancer.

AIMS: Tumour suppressor ING4 is one of ING family genes, which are involved in cell cycle arrest, gene transcription regulation, DNA repair and apoptosis. ING4 inhibition has been reported in various tumours, including gliomas, breast tumours, and stomach adenocarcinoma. The aim was to evaluate ING4 expression in lung cancers. METHOD AND RESULTS: By immunohistochemistry of 246 lung tumour tissues, reduced ING4 nuclear and cytoplasmic expression were both revealed in lung cancer and associated with tumour grade. Interestingly, compared with normal tissues, we found more tumours with ING4 expression in the cytoplasm higher than in the nucleus. Nuclear ING4 inhibition correlated with the tumour stage and lymph node metastasis. Consistent with these findings, semiquantitative reverse transcriptase-polymerase chain reaction and Western blotting demonstrated decreased ING4 mRNA and expression in 100% (50/50) tumour tissues. Furthermore, ING4 expression was lower in grade III than in grades I-II tumours. Reduced ING4 mRNA correlated with lymph node metastasis. CONCLUSIONS: Our results indicate that overall inhibition of ING4 expression and ING4 expression higher in cytoplasm than in nucleus of tumour cells may be involved in the initiation and progression of lung cancers, and thus, analysis for ING4 expression may be useful as a clinical diagnostic and prognostic tool for lung cancer.