[Advances in Biomarkers for Immunotherapy of Non-small Cell Lung Cancer]

Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model

Background

In the treatment of non-small cell lung cancer (NSCLC), recent advances in immunotherapy have heralded a new era. Despite the success of immune therapy, a subset of patients persistently fails to respond. Therefore, to better improve the efficacy of immunotherapy and achieve the purpose of precision therapy, the research and exploration of tumor immunotherapy biomarkers have received much attention.

Methods

Single-cell transcriptomic profiling was used to reveal tumor heterogeneity and the microenvironment in NSCLC. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was utilized to speculate the relative fractions of 22 infiltration immunocyte types in NSCLC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the construction of risk prognostic models and predictive nomograms of NSCLC. Spearman's correlation analysis was employed to explore the relationship between risk score and tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs). Screening of chemotherapeutic agents in the high- and low-risk groups was performed with the "pRRophetic" package in R. Intercellular communication analysis was conducted using the "CellChat" package.

Results

We found that most tumor-infiltrating immune cells were T cells and monocytes. We also found that there was a significant difference in the tumor-infiltrating immune cells and ICIs across different molecular subtypes. Further analysis showed that M0 and M1 mononuclear macrophages were significantly different in different molecular subtypes. The risk prediction model was shown to have to ability to accurately predict the prognosis, immune cell infiltration, and chemotherapy efficacy of patients in the high and low-risk groups. Finally, we found that the carcinogenic effect of migration inhibitory factor (MIF) is mediated by binding to CD74, CXCR4, and CD44 receptors involved in MIF cell signaling.

Conclusions

We have revealed the tumor microenvironment (TME) of NSCLC through single-cell data analysis and constructed a prognosis model of macrophage-related genes. These results could provide new therapeutic targets for NSCLC.

Prognostic Value of Immunotyping Combined with Targeted Therapy in Patients with Non-Small-Cell Lung Cancer and Establishment of Nomogram Model

Objective

Bioinformatics methods were used to analyze non-small-cell lung cancer gene chip data, screen differentially expressed genes (DEGs), explore biomarkers related to NSCLC prognosis, provide new targets for the treatment of NSCLC, and build immunotyping and line-map model.

Methods

NSCLC-related gene chip data were downloaded from the GEO database, and the common DEGs of the two datasets were screened by using the GEO2R tool and FunRich 3.1.3 software. DAVID database was used for GO analysis and KEGG analysis of DEGs, and protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape 3.8.0 software, and the top 20 hub genes were analyzed and screened out. The expression of pivot genes and their relationship with prognosis were verified by multiple external databases.

Results

159 common DEGs were screened from the two datasets. PPI network was constructed and analyzed, and the genes with the top 20 connectivity were selected as the pivotal genes of this study. The results of survival analysis and the patients' survival curve was reflected in the line graph model of NSCLC.

Conclusion

Through the screening and identification of the VIM-AS1 gene, as well as the analysis of immune infiltration and immune typing, the successful establishment of the rosette model has a certain guiding value for the molecular targeted therapy of patients with non-small-cell lung cancer.

miRNAs-mediated overexpression of Periostin is correlated with poor prognosis and immune infiltration in lung squamous cell carcinoma

Lung cancer is one of the most common malignancies with a high mortality rate worldwide. POSTN has been shown to be strongly correlated with the poor prognosis of lung cancer patients. However, the function and mechanism of action of POSTN in lung cancer remain unclear. Here, we carried out a pan-cancer analysis to assess the clinical prognostic value of POSTN based on the TCGA, TIMER, Oncomine, Kaplan-Meier, and UALCAN databases. We found that upregulated POSTN can be a promising biomarker to predict the prognosis of patients with lung cancer. High levels of POSTN correlated with immune cell infiltration in lung cancer, especially lung squamous cell carcinoma (LUSC), which was further confirmed based on the results from the TISIDB database. Moreover, the expression analysis, correlation analysis, and survival analysis revealed that POSTN-targeted miRNAs, downregulation of has-miR-144-3p and has-miR-30e-3p, were significantly linked to poor prognosis in patients with LUSC. Taken together, we identified that POSTN can act as a novel biomarker for determining the prognosis related to immune infiltration in patients with LUSC and deserves further research.