Analyses of multi-omics differences between patients with high and low PD1/PDL1 expression in lung squamous cell carcinoma

The genomic landscape of the immune system in lung cancer: present insights and continuing investigations

Lung cancer is one of the most prevalent malignancies worldwide, contributing to over a million cancer-related deaths annually. Despite extensive research investigating the genetic factors associated with lung cancer susceptibility and prognosis, few studies have explored genetic predispositions regarding the immune system. This review discusses the most recent genomic findings related to the susceptibility to or protection against lung cancer, patient survival, and therapeutic responses. The results demonstrated the effect of immunogenetic variations in immune system-related genes associated with innate and adaptive immune responses, cytokine, and chemokine secretions, and signaling pathways. These genetic diversities may affect the crosstalk between tumor and immune cells within the tumor microenvironment, influencing cancer progression, invasion, and prognosis. Given the considerable variability in the individual immunegenomics profiles, future studies should prioritize large-scale analyses to identify potential genetic variations associated with lung cancer using highthroughput technologies across different populations. This approach will provide further information for predicting response to targeted therapy and promotes the development of new measures for individualized cancer treatment.

PVR/TIGIT and PD-L1/PD-1 expression predicts survival and enlightens combined immunotherapy in lung squamous cell carcinoma

PVR/TIGIT and PD-L1/PD-1 axes play essential roles in tumor immune evasion and could be potential targets for combined immunotherapy. We aimed to evaluate the expression status of the above-mentioned immune markers in lung squamous cell carcinoma (LUSC), and investigate their survival impact and relevance with the immune microenvironment and clinicopathological features. We retrospectively collected specimens from 190 LUSC patients, who underwent pulmonary surgeries, and we performed immunohistochemistry assays of PVR, TIGIT, PD-L1, PD-1 and CD8. In our cohort, the positive rate of PVR was 85.8%, which was much higher than the positive rate of PD-L1 at 26.8%. A total of 32 (16.8%) patients demonstrated co-expression of PVR/PD-L1. High TIGIT density was correlated with positive PD-L1 expression, high PD-1 density, and high CD8 density (PD-L1, P=0.033; PD-1, P<0.001; CD8, P<0.001), and positive PVR expression was correlated with positive PD-L1 expression (P=0.046). High TIGIT density and high PVR/TIGIT expression were correlated with advanced TNM stage (TIGIT density, P=0.020; PVR/TIGIT expression, P=0.041). Patients with positive PVR expression, high TIGIT density, high PVR/TIGIT expression and PVR/PD-L1 co-expression exhibited a significantly worse prognosis (PVR, P=0.038; TIGIT, P=0.027; PVR/TIGIT, P=0.014; PVR/PD-L1, P=0.018). Multivariate analysis demonstrated that PVR/PD-L1 co-expression (Hazard ratio [HR], 1.756, 95% CI, 1.152-2.676, P=0.009) was an independent prognostic factor in LUSC patients. In conclusion, we demonstrated the expression status of PVR/TIGIT and PD-L1/PD-1 in LUSC. PVR/PD-L1 co-expression was an independent prognostic factor in LUSC patients and may serve as a potential predictive biomarker for dual-targeting immunotherapy.

Integrative Analysis of Multi-Omics Data-Identified Key Genes With KLRC3 as the Core in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and were effective for only a small number of cancer patients. Thus, it is important to figure out the issue about the low response rate of immunotherapy. Here, we performed ssGSEA and unsupervised clustering analysis to identify three clusters (clusters A, B, and C) according to different immune cell infiltration status, prognosis, and biological action. Of them, cluster C showed a better survival rate, higher immune cell infiltration, and immunotherapy effect, with enrichment of a variety of immune active pathways including T and B cell signal receptors. In addition, it showed more significant features associated with immune subtypes C2 and C3. Furthermore, we used WGCNA analysis to confirm the cluster C-associated genes. The immune-activated module highly correlated with 111 genes in cluster C. To pick candidate genes in SD/PD and CR/PR patients, we used the least absolute shrinkage (LASSO) and SVM-RFE algorithms to identify the targets with better prognosis, activated immune-related pathways, and better immunotherapy. Finally, our analysis suggested that there were six genes with KLRC3 as the core which can efficiently improve immunotherapy responses with greater efficacy and better prognosis, and our study provided clues for further investigation about target genes associated with the higher response rate of immunotherapy.

Mutations in the TTN Gene are a Prognostic Factor for Patients with Lung Squamous Cell Carcinomas

Purpose

To analyze the relationship between titin (TTN) mutation gene and tumor mutational burden (TMB) and the with prognosis of lung squamous cell carcinomas (LUSC), and to explore the feasibility of TTN as a potential prognostic marker of for LUSC.

Methods

We analyzed the somatic mutation landscape of LUSC samples using datasets obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Sequence data were divided into wild and mutant groups, and differences in TMB values between the groups compared using a Mann–Whitney U-test. The Kaplan Meier method was used to analyze the correlation between TTN mutation and LUSC prognosis, whereas CIBERSORT algorithm was used to calculate the degree of relative enrichment degree of among tumor-infiltrating lymphocytes in LUSC.

Results

Analysis of both datasets revealed high mutations in the TTN gene, with mutants exhibiting a significantly higher TMB value relative to the wild-type (P < 0.001). Prognosis of the TTN mutant group in LUSC was significantly better than that of wild-type (P = 0.009). Kaplan Meier curves showed that TTN mutation may be an independent prognostic factor in LUSC patients (HR: 0.64, 95% CI 0.48–0.85, P = 0.001), while GSEA analysis revealed that TTN mutation plays a potential role in the development of LUSC. Finally, analysis of LUSC immune microenvironment revealed that TTN mutation was significantly associated with enrichment of macrophages M1 (p < 0.05).

Conclusion

TTN mutation is associated with TMB, and is positively correlated with prognosis of LUSC. Therefore, this mutation may serve as a potential prognostic indicator of LUSC.

Differences in genetics and microenvironment of lung adenocarcinoma patients with or without TP53 mutation

Background

Differences in genetics and microenvironment of LUAD patients with or without TP53 mutation were analyzed to illustrate the role of TP53 mutation within the carcinogenesis of LUAD, which will provide new concepts for the treatment of LUAD.

Methods

In this study, we used genetics and clinical info from the TCGA database, including somatic mutations data, RNA-seq, miRNA-seq, and clinical data. More than one bioinformatics tools were used to analyze the unique genomic pattern of TP53-related LUAD.

Results

According to TP53 gene mutation status, we divided the LUAD patients into two groups, including 265 in the mutant group (MU) and 295 in the wild-type group (WT). 787 significant somatic mutations were detected between the groups, including mutations in titin (TTN), type 2 ryanodine receptor (RYR2) and CUB and Sushi multiple domains 3(CSMD3), which were up-regulated in the MU. However, no significant survival difference was observed. At the RNA level, we obtained 923 significantly differentially expressed genes; in the MU, α-defensin 5(DEFA5), pregnancy-specific glycoprotein 5(PSG5) and neuropeptide Y(NPY) were the most up-regulated genes, glucose-6-phosphatase (G6PC), alpha-fetoprotein (AFP) and carry gametocidal (GC) were the most down-regulated genes. GSVA analysis revealed 30 significant pathways. Compared with the WT, the expression of 12 pathways in the mutant group was up-regulated, most of which pointed to cell division. There were significant differences in tumor immune infiltrating cells, such as Macrophages M1, T cells CD4 memory activated, Mast cells resting, and Dendritic cells resting. In terms of immune genes, a total of 35 immune-related genes were screened, of which VGF (VGF nerve growth factor inducible) and PGC (peroxisome proliferator-activated receptor gamma coactivator) were the most significant up-regulated and down-regulated genes, respectively. Research on the expression pattern of immunomodulators found that 9 immune checkpoint molecules and 6 immune costimulatory molecules were considerably wholly different between the two groups.

Conclusions

Taking the mutant group as a reference, LUAD patients in the mutant group had significant differences in somatic mutations, mRNA-seq, miRNA-seq, immune infiltration, and immunomodulators, indicating that TP53 mutation plays a crucial role in the occurrence and development of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01671-8.

Multiomics Differences in Lung Squamous Cell Carcinoma Patients with High Radiosensitivity Index Compared with Those with Low Radiosensitivity Index

Objectives

Radiosensitivity Index (RSI) can predict intrinsic radiotherapy sensitivity. We analyzed multiomics characteristics in lung squamous cell carcinoma between high and low RSI groups, which may help understand the underlying molecular mechanism of radiosensitivity and guide optional treatment for patients in the future.

Methods

The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data were used to download clinical data, mRNA, microRNA, and lncRNA expression. Differential analyses, including mRNA, miRNA, lncRNA, and G.O. and KEGG, and GSVA analyses, were performed with R. Gene set enrichment analysis was done by GSEA. miRNA-differentially expressed gene network and ceRNA network were analyzed and graphed by the Cytoscape software.

Results

In TCGA data, 542 patients were obtained, including 171 in the low RSI group (LRSI) and 371 in the high RSI group (HRSI). In RNAseq, 558 significantly differentially expressed genes (DEGs) were obtained. KRT6A was the most significantly upregulated gene and IDO1 was the most significantly downregulated gene. In miRNAseq, miR-1269a was the most significantly upregulated. In lncRNAseq, LINC01871 was the most upregulated. A 66-pair interaction between differentially expressed genes and miRNAs and an 11-pair interaction between differential lncRNAs and miRNAs consisted of a ceRNA network, of which miR-184 and miR-490-3p were located in the center. In the GEO data, there were 40 DEGs. A total of 17 genes were founded in both databases, such as ADAM23, AHNAK2, BST2, COL11A1, CXCL13, FBN2, IFI27, IFI44L, MAGEA6, and PTGR1. GSVA analysis revealed 31 significant pathways. GSEA found 87 gene sets enriched in HRSI and 91 gene sets in LRSI. G.O. and KEGG of RNA expression levels revealed that these genes were most enriched in T cell activation and cytokine-cytokine receptor interaction.

Conclusions

Patients with lung squamous cell carcinoma have different multiomics characteristics between two groups. These differences may have an essential significance with radiotherapy effect.