A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer.

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post-translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five-mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease-free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985-7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138-3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186-0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM-related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.

Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer.

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC. Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis. Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC. Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer.

Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Lymph node metastasis (LNM) is a common mode of metastasis of CRC. However, the combined mRNA biomarkers associated with LNM of CRC that can effectively predict CRC prognosis have not been reported yet. Methods: To identify biomarkers that are associated with LNM, we collected data from the The Cancer Genome Atlas (TCGA) database. The edgeR package was searched to seek LNM-related genes by comparisons between cancer samples and normal colorectal tissues and between LNM and non-LNM (NLNM) of CRC. Univariate and multivariate regression analysis of genes in the intersection to build gene signature associated with independent prognosis of CRC, and then verified by Kaplan-Meier curve and log-rank test, receiver operating characteristic (ROC) curve was used to determine the efficiency of survival prediction of our four-mRNA signature. Finally, the potential molecular mechanisms and properties of these gene signature were also explored with functional and pathway enrichment analysis. Results: 329 mRNAs were up-regulated in CRC tissues with LNM, and 8461 mRNAs were up-regulated in CRC tissues, the intersection is 100 mRNAs. After univariate and multivariate Cox regression analysis of 100 mRNAs, a novel four LNM related mRNAs (EPHA8, KRT85, GABRA3, and CLPSL1) were screened as independent prognostic indicators of CRC. Surprisingly, the four-mRNA signature can predict the prognosis of CRC patients independently of clinical factors andthe area under the curve (AUC) of the ROC is 0.730. The novel four-mRNA signature was used to identify high and low-risk groups. Stratified analysis indicated the risk score based on four-mRNA signature was an independent prognostic indicator for female, T3+T4, N1+N2 ,stage III+IV and patients with no new tumor event. Functional annotation of this risk model in high-risk patients revealed that pathways associated with neuroactive ligand-receptor interaction, estrogen signaling pathway, and steroid hormone biosynthesis. Conclusions: By conducting TCGA data mining, our study demonstrated that a four-mRNA signature associated with LNM can be used as a combined biomarker for independent prognosis of CRC.

Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma.

Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.