Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression

Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

Considering the key role of myeloid cell differentiation-related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, "edgeR" R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation-related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients.

Prognostic characteristics of a six-gene signature based on ssGSEA in sarcoma

BACKGROUND

Sarcoma is a rare malignant tumor originating of the interstitial or connective tissue with a poor prognosis. Next-generation sequencing technology offers new opportunities for accurate diagnosis and treatment of sarcomas. There is an urgent need for new gene signature to predict prognosis and evaluate treatment outcomes.

METHODS

We used transcriptome data from the Cancer Genome Atlas (TCGA) database and single sample gene set enrichment analysis (ssGSEA) to explore the cancer hallmarks most associated with prognosis in sarcoma patients. Then, weighted gene coexpression network analysis, univariate COX regression analysis and random forest algorithm were used to construct prognostic gene characteristics. Finally, the prognostic value of gene markers was validated in the TCGA and Integrated Gene Expression (GEO) (GSE17118) datasets, respectively.

RESULTS

MYC targets V1 and V2 are the main cancer hallmarks affecting the overall survival (OS) of sarcoma patients. A six-gene signature including VEGFA, HMGB3, FASN, RCC1, NETO2 and BIRC5 were constructed. Kaplan-Meier analysis suggested that higher risk scores based on the six-gene signature associated with poorer OS (P < 0.001). The receiver Operating characteristic curve showed that the risk score based on the six-gene signature was a good predictor of sarcoma, with an area under the curve (AUC) greater than 0.73. In addition, the prognostic value of the six-gene signature was validated in GSE17118 with an AUC greater than 0.72.

CONCLUSION

This six-gene signature is an independent prognostic factor in patients with sarcoma and is expected to be a potential therapeutic target for sarcoma.

Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma

BACKGROUND

This study aims to construct a new prognostic gene signature in survival prediction and risk stratification for patients with Head and neck squamous cell carcinoma (HNSCC).

METHOD

The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hallmarks most relevant to the prognosis of HNSCC patients. Differential gene expression analysis, weighted gene co-expression network analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct the prognostic gene signature. The predictive ability of gene signature was verified in the TCGA HNSCC cohort as the training set and the GEO HNSCC cohorts (GSE41613 and GSE42743) as the validation sets, respectively. Moreover, the correlations between risk scores and immune infiltration patterns, as well as risk scores and genomic changes were explored.

RESULTS

A total of 3391 differentially expressed genes in HNSCC were screened. Glycolysis and hypoxia were screened as the main risk factors for OS in HNSCC. Using univariate Cox analysis, 97 prognostic candidates were identified (P < 0.05). Top 10 important genes were then screened out by random forest. Using multiple combinatorial screening, a combination with less genes and more significant P value was used to construct the prognostic gene signature (RNF144A, STC1, P4HA1, FMNL3, ANO1, BASP1, MME, PLEKHG2 and DKK1). Kaplan-Meier analysis showed that patients with higher risk scores had worse overall survival (p < 0.001). The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66). Subsequently, the predictive ability of the risk score was verified in the validation sets. Moreover, the two-factor survival analysis combining the cancer hallmarks and risk scores suggested that HNSCC patients with the high hypoxia or glycolysis & high risk-score showed the worst prognosis. Besides, a nomogram based on the nine-gene signature was established for clinical practice. Furthermore, the risk score was significantly related to tumor immune infiltration profiles and genome changes.

CONCLUSION

This nine-gene signature associated with glycolysis and hypoxia can not only be used for prognosis prediction and risk stratification, but also may be a potential therapeutic target for patients with HNSCC.