Identification of a Transcription Factor Signature That Can Predict Breast Cancer Survival

Transcription factors-related molecular subtypes and risk prognostic model: exploring the immunogenicity landscape and potential drug targets in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, with a high mortality rate and poor prognosis. Mutated or dysregulated transcription factors (TFs) are significantly associated with carcinogenesis. The aim of this study was to develop a TF-related prognostic risk model to predict the prognosis and guide the treatment of HCC patients.

METHODS

RNA sequencing data were obtained from the TCGA database. The ICGC and GEO databases were used as validation datasets. The consensus clustering algorithm was used to classify the molecular subtypes of TFs. Kaplan‒Meier survival analysis and receiver operating characteristic (ROC) analysis were applied to evaluate the prognostic value of the model. The immunogenic landscape differences of molecular subtypes were evaluated by the TIMER and xCell algorithms. Autodock analysis was used to predict possible binding sites of trametinib to TFs. RT‒PCR was used to verify the effect of trametinib on the expression of core TFs.

RESULTS

According to the differential expression of TFs, HCC samples were divided into two clusters (C1 and C2). The survival time, signaling pathways, abundance of immune cell infiltration and responses to chemotherapy and immunotherapy were significantly different between C1 and C2. Nine TFs with potential prognostic value, including HMGB2, ESR1, HMGA1, MYBL2, TCF19, E2F1, FOXM1, CENPA and ZIC2, were identified by Cox regression analysis. HCC patients in the high-risk group had a poor prognosis compared with those in the low-risk group (p < 0.001). Moreover, the area under the ROC curve (AUC) values of the 1-year, 2-year and 3-year survival rates were 0.792, 0.71 and 0.695, respectively. The risk model was validated in the ICGC database. Notably, trametinib sensitivity was highly correlated with the expression of core TFs, and molecular docking predicted the possible binding sites of trametinib with these TFs. More importantly, the expression of core TFs was downregulated under trametinib treatment.

CONCLUSIONS

A prognostic signature with 9 TFs performed well in predicting the survival rate and chemotherapy/immunotherapy effect of HCC patients. Trimetinib has potential application value in HCC by targeting TFs.

Identification of a Novel Gene Signature with DDR and EMT Difunctionalities for Predicting Prognosis, Immune Activity, and Drug Response in Breast Cancer

Breast cancer, with an overall poor clinical prognosis, is one of the most heterogeneous cancers. DNA damage repair (DDR) and epithelial-mesenchymal transition (EMT) have been identified to be associated with cancer's progression. Our study aimed to explore whether genes with both functions play a more crucial role in the prognosis, immune, and therapy response of breast cancer patients. Based on the Cancer Genome Atlas (TCGA) cancer database, we used LASSO regression analysis to identify the six prognostic-related genes with both DDR and EMT functions, including TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2. Based on the six genes, we defined the risk scores of the patients and reasonably analyzed the overall survival rate between the patients with the different risk scores. We found that overall survival in higher-risk-score patients was lower than in lower-risk-score patients. Subsequently, further GO and KEGG analyses for patients revealed that the levels of immune infiltration varied for patients with high and low risk scores, and the high-risk-score patients had lower immune infiltration's levels and were insensitive to treatment with chemotherapeutic agents. Furthermore, the Gene Expression Omnibus (GEO) database validated our findings. Our data suggest that TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2 can be potential genetic markers of prognostic assessment, immune infiltration and chemotherapeutic drug sensitivity in breast cancer patients.

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

Gliomas that are classified as grade II or grade III lesions by the World Health Organization (WHO) are highly aggressive, and some may develop into glioblastomas within a short period, thus portending the conferral of a poor prognosis for patients. Previous studies have implicated basement membrane (BM)-related genes in glioma development. In this study, we constructed a prognostic model for WHO grade II/III gliomas in accordance with the risk scores of BM-related genes. Differentially expressed genes (DEGs) in the glioma samples relative to normal samples were screened from the GEO database, and five prognostically relevant BM-related genes, including NELL2, UNC5A, TNC, CSPG4, and SMOC1, were selected using Cox regression analyses for the risk score model. The median risk score was calculated, based on which high- and low-risk groups of patients were generated. The clinical information, pathological information, and risk group were combined to establish a prognostic nomogram. Both the nomogram and risk score model performed well in the independent CGGA cohort. Gene set enrichment analysis (GSEA) and immune profile, drug sensitivity, and tumor mutation burden (TMB) analyses were performed in the two risk groups. A significant enrichment of 'Autophagy-other', 'Collecting duct acid secretion', 'Glycosphingolipid biosynthesis-lacto and neolacto series', 'Valine, leucine, and isoleucine degradation', 'Vibrio cholerae infection', and other pathways were observed for patients with high risk. In addition, higher proportions of monocytes and resting CD4 memory T cells were observed in the low- and high-risk groups, respectively. In conclusion, the BM-related gene risk score model can guide the clinical management of WHO grade II and III gliomas.

Identification and validation of a novel prognostic signature based on transcription factors in breast cancer by bioinformatics analysis

BACKGROUND

Breast cancer (BRCA) is the leading cause of cancer mortality among women, and it is associated with many tumor suppressors and oncogenes. There is increasing evidence that transcription factors (TFs) play vital roles in human malignancies, but TFs-based biomarkers for BRCA prognosis were still rare and necessary. This study sought to develop and validate a prognostic model based on TFs for BRCA patients.

METHODS

Differentially expressed TFs were screened from 1,109 BRCA and 113 non-tumor samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis was used to identify TFs associated with overall survival (OS) of BRCA, and multivariate Cox regression analysis was performed to establish the optimal risk model. The predictive value of the TF model was established using TCGA database and validated using a Gene Expression Omnibus (GEO) data set (GSE20685). A gene set enrichment analysis was conducted to identify the enriched signaling pathways in high-risk and low-risk BRCA patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the TF target genes were also conducted separately.

RESULTS

A total of 394 differentially expressed TFs were screened. A 9-TF prognostic model, comprising PAX7, POU3F2, ZIC2, WT1, ALX4, FOXJ1, SPIB, LEF1 and NFE2, was constructed and validated. Compared to those in the low-risk group, patients in the high-risk group had worse clinical outcomes (P<0.001). The areas under the curve of the prognostic model for 5-year OS were 0.722 in the training cohort and 0.651 in the testing cohort. Additionally, the risk score was an independent prediction indicator for BRCA patients both in the training cohort (HR =1.757, P<0.001) and testing cohort (HR =1.401, P=0.001). It was associated with various cancer signaling pathways. Ultimately, 9 overlapping target genes were predicted by 3 prediction nomograms. The GO and KEGG enrichment analyses of these target genes suggested that the TFs in the model may regulate the activation of some classical tumor signaling pathways to control the progression of BRCA through these target genes.

CONCLUSIONS

Our study developed and validated a novel prognostic TF model that can effectively predict 5-year OS for BRCA patients.