Microenvironment and related genes predict outcomes of patients with cervical cancer: evidence from TCGA and bioinformatic analysis

Cervical cancer (CESC) is one of the most common cancers and affects the female genital tract. Consistent HPV infection status has been determined to be a vital cause of tumorigenesis. HPV infection may induce changes to the immune system and limit the host’s immune response. Immunotherapy is therefore essential to improving the overall survival of both locally advanced and recurrent CESC patients. Using 304 relevant samples from TCGA, we assessed immune cell function in CESC patients to better understand the status of both tumor micro-environment cells and immune cells in CESC. Functional enrichment analysis, pathway enrichment analysis, and PPI network construction were performed to explore the differentially expressed genes (DEGs). The analysis identified 425 DEGs, which included 295 up-regulated genes and 130 down-regulated genes. We established that upregulation of CCL5 was correlated with significantly better survival, meaning that CCL5 expression could serve as a novel prognostic biomarker for CESC patients. We further focused on CCL5 as a hub gene in CESC, as it had significant correlations with increased numbers of several types of immune cells. Cell-type fractions of M1 macrophages were significantly higher in the high-immune-scores group, which was associated with better overall survival. Finally, we concluded that CCL5 is a promising prognostic biomarker for CESC, as well as a novel chemotherapeutic target.     

A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual’s whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.     

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.     

Identification of Tumor-Suppressive miR-30e-3p Targets: Involvement of SERPINE1 in the Molecular Pathogenesis of Head and Neck Squamous Cell Carcinoma

Recently, our studies revealed that some passenger strands of microRNAs (miRNAs) were closely involved in cancer pathogenesis. Analysis of miRNA expression signatures showed that the expression of miR-30e-3p (the passenger strand of pre-miR-30e) was significantly downregulated in cancer tissues. In this study, we focused on miR-30e-3p (the passenger strand of pre-miR-30e). We addressed target genes controlled by miR-30e-3p that were closely associated with the molecular pathogenesis of head and neck squamous cell carcinoma (HNSCC). Ectopic expression assays demonstrated that the expression of miR-30e-3p attenuated cancer cell malignant phenotypes (e.g., cell proliferation, migration, and invasive abilities). Our analysis of miR-30e-3p targets revealed that 11 genes (ADA, CPNE8, C14orf126, ERGIC2, HMGA2, PLS3, PSMD10, RALB, SERPINE1, SFXN1, and TMEM87B) were expressed at high levels in HNSCC patients. Moreover, they significantly predicted the short survival of HNSCC patients based on 5-year overall survival rates (p < 0.05) in The Cancer Genome Atlas (TCGA). Among these targets, SERPINE1 was found to be an independent prognostic factor for patient survival (multivariate Cox regression; hazard ratio = 1.6078, p < 0.05). Aberrant expression of SERPINE1 was observed in HNSCC clinical samples by immunohistochemical analysis. Functional assays by targeting SERPINE1 expression revealed that the malignant phenotypes (e.g., proliferation, migration, and invasion abilities) of HNSCC cells were suppressed by the silencing of SERPINE1 expression. Our miRNA-based approach will accelerate our understanding of the molecular pathogenesis of HNSCC.     

A novel m6A‐related prognostic signature for predicting the overall survival of hepatocellular carcinoma patients

Liver hepatocellular carcinoma (LIHC) comprises most cases of liver cancer with a poor prognosis. N ⁶‐methyladenosine (m6A) plays important biological functions in cancers. Thus, the present research was aimed to determine biomarkers of m6A regulators that could effectively predict the prognosis of LIHC patients. Based on the data collected from the Cancer Genome Atlas (TCGA) database, the correlation between the mRNA expression levels and copy number variation (CNV) patterns were determined. Higher mRNA expression resulted from the increasing number of 9 genes. Using the univariate Cox regression analysis, 11 m6A regulators that had close correlations with the LIHC prognosis were identified. In addition, under the support of the multivariate Cox regression models and the least absolute shrinkage and selection operator, a 4‐gene (YTHDF2, IGF2BP3, KIAA1429, and ALKBH5) signature of m6A regulators was constructed. This signature was expected to present a prognostic value in LIHC (log‐rank test p value < 0.0001). The {"type":"entrez-geo","attrs":{"text":"GSE76427","term_id":"76427"}}GSE76427 (n = 94) and ICGC‐LIRI‐JP (n = 212) datasets were used to validate the prognostic signature, suggesting strong power to predict patients'' prognosis for LIHC. To sum up, genetic alterations in m6A regulatory genes were identified as reliable and effective biomarkers for predicting the prognosis of LIHC patients.