A seven-gene prognostic model related to immune checkpoint PD-1 revealing overall survival in patients with lung adenocarcinoma

Novel immune checkpoint-related gene model to predict prognosis and treatment responsiveness in low-grade gliomas

Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining immune homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation in low-grade glioma (LGG) cases. In the present study, a unique prognostic gene signature in LGG has been identified and validated as well based on ICGs as a means of facilitating clinical decision-making. The RNA-seq data as well as corresponding clinical data of LGG samples have been retrieved utilizing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with LGG into two molecular subtypes with different prognoses, clinical traits, and immune microenvironments. In the TCGA database, a signature integrating 8 genes has been developed utilizing the LASSO Cox method and validated in the GEO database. The signature developed is superior to other well-recognized signatures in terms of predicting the survival probability of patients with LGG. This 8-gene signature was then subsequently applied to categorize patients into high- and low-risk groups, and differences between them in terms of gene alteration frequency were observed. There were remarkable variations in IDH1 (91% and 64%) across low-as well as high-risk groups. Additionally, various analyses like function enrichment, tumor immune microenvironment, and chemotherapy drug sensitivity revealed significant variations across high- and low-risk populations. Overall, this 8-gene signature may function as a useful tool for prognosis and immunotherapy outcome predictions among LGG patients.

Genomic and transcriptomic profiling reveals key molecules in metastatic potentials and organ-tropisms of hepatocellular carcinoma

Metastasis is a landmark event for rapid postsurgical relapse and death of HCC patients. Although distinct genomic and transcriptomic profiling of HCC metastasis had been reported previously, the causal relationships of somatic mutants, mRNA levels and metastatic potentials were difficult to be established in clinic. Therefore, 11 human HCC cell lines and 7 monoclonal derivatives with definite metastatic potentials and tropisms were subjected to whole exome sequencing (WES) and whole transcriptome sequencing (WTS). TP53, MYO5A, ROS1 and ARID2 were the prominent mutants of metastatic drivers in HCC cells. During HCC clonal evaluation, TP53, MYO5A and ROS1 mutations occurred in the early stage, EXT2 and NIN in the late stage. NF1 mutant was unique in lung tropistic cell lines, RNF126 mutant in lymphatic tropistic ones. PER1, LMO2, GAS7, NR4A3 expression levels were positively associated with relapse-free survival (RFS) of HCC patients. The integrative analysis revealed 58 genes exhibited both somatic mutation and dysregulated mRNA levels in high metastatic cells. Altogether, metastatic drivers could accumulate gradually at different stages during HCC progression, some drivers might modulate HCC metastatic potentials and the others regulate metastatic tropisms.

A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas

BACKGROUND

Lung adenocarcinoma (LUAD) remains a global health concern with its poor prognosis and high mortality. Whether tumor cells invade through the basement membrane (BM) is the key factor to determine the prognosis of LUAD. This study aimed to identify the BM-related gene signatures to improve the overall prognosis of LUAD.

MATERIALS & METHODS

A series of bioinformatics analyses were conducted based on TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed, and 500 LUAD patients were assigned to two different groups according to expressions of 222 BM-related genes. The differentially expressed genes (DEGs) between the two clusters were identified, and Lasso regression, ROC curve, univariate and multivariate Cox regression analyses and enrichment analysis were conducted. Besides, ssGSEA, CIBERSORT and ESTIMATE algorithmwere were employed to understand the relationship between the tumor microenvironment (TME) and risk scores. Moreover, single cell clustering and trajectory analyses were performed to further understand the significance of BM-related genes. Finally, qRT-PCR was used to verify the prognosis model.

RESULTS

A total of 31 prognostic BM-related genes were determined for LUAD, and a novel 17-mRNA prognostic model named BMsocre was successfully established to predict the overall survival of LUAD patients. The high BMscore group indicated worse prognosis. Seventeen DEGs were enriched mainly in metabolism, ECM-receptor interaction and immune response. In addition, the high-risk group showed higher TMB and lower immune score. The low-risk group had a better immunotherapeutic response where immune escape was less likely. The BMscore model was verified in our patient cohort. Furthermore, NELL2 was mainly expressed in clusters of T cells, and was identified to play a critical role in T-cell differentiation.

CONCLUSIONS

A novel BMscore model was successfully established and might be effective for providing guidance to LUAD therapy.