A Comprehensive Molecular and Clinical Analysis of the piRNA Pathway Genes in Ovarian Cancer

Construction of prognostic markers for pancreatic adenocarcinoma based on mitochondrial fusion-related genes

Early detection of pancreatic adenocarcinoma (PAAD) remains a pressing clinical problem. Information on the clinical prognostic value of mitochondrial fusion-related genes in PAAD remains limited. In this study, we investigated mitochondrial fusion-related genes of PAAD to establish an optimal signature plate for the early diagnosis and prognosis of PAAD. The cancer genome atlas database was used to integrate the Fragments Per Kilobase Million data and related clinical data for patients with PAAD. Least absolute shrinkage and selection operator regression, cox regression, operating characteristic curves, and cBioPortal database was used to evaluate model performance, assess the prognostic ability and sensitivity. The levels of immune infiltration were compared by CIBERSORT, QUANTISEQ, and EPIC. Chemotherapy sensitivity between the different risk groups was compared by the Genomics of Drug Sensitivity in Cancer database and the "pRRophetic" R package. At last, a total of 4 genes were enrolled in multivariate Cox regression analysis. The risk-predictive signature was constructed as: (0.5438 × BAK1) + (-1.0259 × MIGA2) + (1.1140 × PARL) + (-0.4300 × PLD6). The area under curve of these 4 genes was 0.89. Cox regression analyses indicates the signature was an independent prognostic indicator (P < .001, hazard ratio [HR] = 1.870, 95% CI = 1.568-2.232). Different levels of immune cell infiltration in the 2 risk groups were observed using the 3 algorithms, with tumor mutation load (P = .0063), tumor microenvironment score (P = .01), and Tumor Immune Dysfunction and Exclusion score (P = .0012). The chemotherapeutic sensitivity analysis also revealed that the half-maximal inhibitory concentration of 5-fluorouracil (P = .0127), cisplatin (P = .0099), docetaxel (P < .0001), gemcitabine (P = .0047), and pacilataxel (P < .0001) were lower in the high-risk groups, indicating that the high-risk group patients had a greater sensitivity to chemotherapy. In conclude, we established a gene signature plate comprised of 4 mitochondrial fusion related genes to facilitate early diagnosis and prognostic prediction of PAAD.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Background

Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods

A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results

A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions

A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

Screening of potential key genes in esophageal cancer based on RBP and expression verification of HENMT1

To screen key biomarkers of esophageal cancer (ESCA) by bioinformatics and analyze the correlation between key genes and immune infiltration. Expression profile data of ESCA was downloaded from TCGA database, and DEGs in ESCA were screened with R software. After the RNA binding proteins (RBPs) in DEGs were screened, the protein interaction network was constructed using tools such as STRING and Cytoscape and the key genes (HENMT1) were screened. Survival analysis of HENMT1 was performed by Kaplan-Meier method. Functional enrichment analysis of HENMT1 interacting proteins was performed using the DAVID website, and GSEA predicted the signal pathways involved by HENMT1. CIBERSORT algorithm was used to analyze the infiltration of immune cells in ESCA. The expression of HENMT1 in ESCA was detected by immunohistochemistry. A total of 105 RNA binding proteins (RBPs) were differentially expressed in ESCA, and a PPI network was constructed to screen the key gene HENMT1. The expression level of hemmt1 gene was closely related to the infiltration of B cells naive, T cells regulatory (Tregs), neutrophils, T cells CD4 memory activated, master cells resting and dendritic cells resting in ESCA tissues (P < .05). Immunohistochemical results showed that HENMT1 was highly expressed in ESCA tissues and was positively correlated with the expression of MKI67. HENMT1 is related to the occurrence and prognosis of ESCA, and is also related to the infiltration of immune cells in ESCA tissue, which may provide a new idea for the targeted treatment of ESCA.

Exploring Potential Regulatory Anesthetic Drugs Based on RNA Binding Protein and Constructing CESC Prognosis Model: A Study Based on TCGA Database

Objective

To investigate the differential expression of RBPs in cervical squamous cell carcinoma (CESC), analyze the regulatory effect of narcotic drugs on RBPs, and establish the prognostic risk model of CESC patients.

Methods

RNA-SEQ data and clinical case data of cancer and normal samples from CESC patients were obtained from the Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database. Differentially expressed RBPs were screened by R language and enriched. The CMAP database is used to predict the anesthetic drugs that regulate the differential expression of RBPs. The prognostic risk score model was constructed by COX regression analysis. Risk score of each CESC patient was calculated and divided into high-risk group and low-risk group according to the median risk score. The prediction efficiency of prognostic risk model was evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve, and the correlation between prognostic risk model and clinical characteristics was analyzed. Immunohistochemistry was used to detect the expression of RNASEH2A and HENMT1 in tissues.

Results

There were 65 differentially expressed RBPs in CESC. Five anesthetics, including benzocaine, procaine, pentoxyverine, and tetracaine were obtained to regulate RBPs. Survival analysis showed that seven genes were related to the prognosis of patients, and the CESC risk score model was constructed by COX regression. The risk score can be used as an independent prognostic factor. RNASEH2A and HENMT1 are up-regulated in tumors, which can effectively distinguish normal tissues from tumor tissues.

Conclusion

It is found that different anesthetic drugs have different regulatory effects on the differential expression of RBPs. Based on the differentially expressed RBPs, the prognostic risk score model of CESC patients was constructed. To provide ideas for the formulation of individualized precise anesthesia scheme and cancer pain analgesia scheme, which is helpful to improve the perioperative survival rate of cancer patients.