Correlation of immune infiltration with clinical outcomes in breast cancer patients: The 25-gene prognostic signatures model

The Clinical and Biological Effects of Receptor Expression-Enhancing Protein 6 in Tongue Squamous Cell Carcinoma

There are currently no effective biomarkers for the diagnosis and treatment of tongue squamous cell carcinoma (TSCC), which causes a poor 5-year overall survival rate. Thus, it is crucial to identify more effective diagnostic/prognostic biomarkers and therapeutic targets for TSCC patients. The receptor expression-enhancing protein 6 (REEP6), a transmembrane endoplasmic reticulum resident protein, controls the expression or transport of a subset of proteins or receptors. Although it was reported that REEP6 plays a role in lung and colon cancers, its clinical impact and biological role in TSCC are still unknown. The present study aimed to identify a novel effective biomarker and therapeutic target for TSCC patients. Expression levels of REEP6 in specimens from TSCC patients were determined with immunohistochemistry. Gene knockdown was used to evaluate the effects of REEP6 in cancer malignancy (colony/tumorsphere formation, cell cycle regulation, migration, drug resistance and cancer stemness) of TSCC cells. The clinical impact of REEP6 expression and gene co-expression on prognosis were analyzed in oral cancer patients including TSCC patients from The Cancer Genome Atlas database. Tumor tissues had higher levels of REEP6 compared to normal tissues in TSCC patients. Higher REEP6 expression was related to shorter disease-free survival (DFS) in oral cancer patients with poorly differentiated tumor cells. REEP6-knocked-down TSCC cells showed diminished colony/tumorsphere formation, and they also caused G1 arrest and decreased migration, drug resistance and cancer stemness. A high co-expression of REEP6/epithelial-mesenchymal transition or cancer stemness markers also resulted in poor DFS in oral cancer patients. Thus, REEP6 is involved in the malignancy of TSCC and might serve as a potential diagnostic/prognostic biomarker and therapeutic target for TSCC patients.

Comprehensive analysis of the immune implication of FABP4 in colon adenocarcinoma

Background

Fatty acid-binding protein 4 (FABP4) has been reported to be associated with tumor progress and poor prognosis in various cancers. However, the relationship between FABP4 expression and tumor immunity in colon adenocarcinoma (COAD) is still poorly understood.

Methods

FABP4 mRNA expression was analyzed using The Cancer Genome Atlas (TCGA)-COAD data. FABP4 protein staining was performed by immunohistochemistry (IHC) staining in our 10 paired COAD samples and corresponding adjacent noncancerous tissues. The association between FABP4 and immune cell infiltration was evaluated by Tumor Immune Estimation Resource (TIMER) database. FABP4 coexpressed genes were identified based on Cancer Cell Line Encyclopedia (CCLE) database, which were employed for further enrichment analysis. FABP4 related immunomodulators was identified by Tumor and Immune System Interaction Database (TISIDB) database, and a prognostic risk signature was constructed based on FABP4-related immunomodulators using stepwise Cox regression analysis. A nomogram consists of FABP4 related immunomodulators signature and clinical parameters was developed to predict the overall survival (OS).

Results

In TCGA data, we found that the decreased FABP4 mRNA expression in COAD samples compared with normal samples, and low FABP4 mRNA expression was associated with B cells, CD4+ T cells, CD8+ T cells, myeloid dendritic cells, macrophages, and neutrophils. In our 10 paired samples, the protein levels of COAD were lower in all COAD tissues than in their adjacent noncancerous tissues. Functional enrichment analysis revealed that FABP4 coexpressed genes were mostly enriched in immune-related pathways. Based on 54 FABP4-related immunomodulators, a 2-gene FABP4-related prognostic risk signature was developed, and the signature stratified the patients into the high-risk and low-risk groups with statistically different survival outcomes. The Nomogram consists of the prognostic signature and clinical parameters had a certain predictability for prognosis of COAD patients.

Conclusion

These findings suggest that FABP4 is associated with 2-gene immune signature which also correlate with the prognosis of COAD patients.

Correlation of immune infiltration with clinical outcomes in breast cancer patients: The 25-gene prognostic signatures model

PURPOSE

Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.

METHODS

The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.

RESULTS

Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed.

CONCLUSIONS

This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.