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Identification of PANoptosis-Based Prognostic Signature for Predicting Efficacy of Immunotherapy and Chemotherapy in Hepatocellular Carcinoma
Background. PANoptosis has been a research hotspot, but the role of PANoptosis in hepatocellular carcinoma (HCC) remains widely unknown. Drug resistance and low response rate are the main limitations of chemotherapy and immunotherapy in HCC. Thus, construction of a prognostic signature to predict prognosis and recognize ideal patients for corresponding chemotherapy and immunotherapy is necessary. Method. The mRNA expression data of HCC patients was collected from TCGA database. Through LASSO and Cox regression, we developed a prognostic signature based on PANoptosis-related genes. KM analysis and ROC curve were implemented to evaluate the prognostic efficacy of this signature, and ICGC and GEO database were used as external validation cohorts. The immune cell infiltration, immune status, and IC50 of chemotherapeutic drugs were compared among different risk subgroups. The relationships between the signature and the efficacy of ICI therapy, sorafenib treatment, and transcatheter arterial chemoembolization (TACE) therapy were investigated. Result. A 3-gene prognostic signature was constructed which divided the patients into low- and high-risk subgroups. Low-risk patients had better prognosis, and the risk score was proved to be an independent predictor of overall survival (OS), which had a well predictive effect. Patients in high-risk population had more immunosuppressive cells (Tregs, M0 macrophages, and MDSCs), higher TIDE score and TP53 mutation rate, and elevated activity of base excision repair (BER) pathways. Patients with low risk benefited more from ICI, TACE, and sorafenib therapy. The predictive value of the risk score was comparable with TIDE and MSI for OS under ICI therapy. The risk score could be a biomarker to predict the response to ICI, TACE, and sorafenib therapy. Conclusion. The novel signature based on PANoptosis is a promising biomarker to distinguish the prognosis predict the benefit of ICI, TACE, and sorafenib therapy, and forecast the response to them.
Bioinformatics-Based Identification of CircRNA-MicroRNA-mRNA Network for Calcific Aortic Valve Disease
Background. Calcific aortic valve disease (CAVD) is the most common native valve disease. Valvular interstitial cell (VIC) osteogenic differentiation and valvular endothelial cell (VEC) dysfunction are key steps in CAVD progression. Circular RNA (circRNAs) is involved in regulating osteogenic differentiation with mesenchymal cells and is associated with multiple disease progression, but the function of circRNAs in CAVD remains unknown. Here, we aimed to investigate the effect and potential significance of circRNA-miRNA-mRNA networks in CAVD. Methods. Two mRNA datasets, one miRNA dataset, and one circRNA dataset of CAVD downloaded from GEO were used to identify DE-circRNAs, DE-miRNAs, and DE-mRNAs. Based on the online website prediction function, the common mRNAs (FmRNAs) for constructing circRNA-miRNA-mRNA networks were identified. GO and KEGG enrichment analyses were performed on FmRNAs. In addition, hub genes were identified by PPI networks. Based on the expression of each data set, the circRNA-miRNA-hub gene network was constructed by Cytoscape (version 3.6.1). Results. 32 DE-circRNAs, 206 DE-miRNAs, and 2170 DE-mRNAs were identified. Fifty-nine FmRNAs were obtained by intersection. The KEGG pathway analysis of FmRNAs was enriched in pathways in cancer, JAK-STAT signaling pathway, cell cycle, and MAPK signaling pathway. Meanwhile, transcription, nucleolus, and protein homodimerization activity were significantly enriched in GO analysis. Eight hub genes were identified based on the PPI network. Three possible regulatory networks in CAVD disease were obtained based on the biological functions of circRNAs including: hsa_circ_0026817-hsa-miR-211-5p-CACNA1C, hsa_circ_0007215-hsa-miR-1252-5p-MECP2, and hsa_circ_0007215-hsa-miR-1343-3p- RBL1. Conclusion. The present bionformatics analysis suggests the functional effect for the circRNA-miRNA-mRNA network in CAVD pathogenesis and provides new targets for therapeutics.
Systematically Prognostic Analyses of Gastric Cancer Patients with Ovarian Metastasis
Ovarian metastasis of gastric cancer indicates that the disease has reached the late stage and the opportunity for radical surgery is restricted. However, the clinical characteristics and prognosis of patients with gastric cancer ovarian metastasis (GCOM) remain to be illustrated. Here, we retrieved the information of 780 GCOM cases from the Surveillance, Epidemiology, and End Results (SEERs) database and analyzed their clinicopathological characteristics as well as their survival. According to our data, most GCOM patients showed poor pathological differentiation, advanced T and N stages. The prognostic factors include patients’ age, tumor size, surgical resection, and chemotherapy treatment. Of note, the marriage status was also identified as an independent prognostic factor. Besides the identification of prognostic factors, we established nomograms to help predict the overall survival and cancer-specific survival of GCOM, respectively.
Regulation of Hepatocytes in G0 and G1 Phases by NOTCH3 mRNA, miR-369-3p, and rno-Rmdn2_0006 during the Initial Stage of Rat Liver Regeneration
The key event of liver regeneration initiation (LRI) is the switch of hepatocytes from the G0 phase to the G1 phase. This study aimed to use the data from large-scale quantitatively detecting and analyzing (LQDA) to reveal the regulation of hepatocytes in the G0 or G1 phase by competing endogenous RNAs (ceRNAs) during LRI. The hepatocytes of the rat liver right lobe were isolated 0, 6, and 24 h after partial hepatectomy. Their ceRNA expression level was measured using LQDA, and the correlation among their expression, interaction, and role was revealed by ceRNA comprehensive analysis. The expression of neurogenic loci notch homologous protein 3 (NOTCH3) mRNA was upregulated in 0 h, but the expression of miR-369-3p and rno-Rmdn2_0006 of hepatocytes did not change significantly. Meanwhile, the expression of the G0 phase-related gene CDKN1c was promoted by NOTCH3 upregulation, and the expression of the G1 phase-related gene PSEN2 was inhibited by NOTCH3 downregulation. On the contrary, the expression of NOTCH3 mRNA and rno-Rmdn2_0006 was upregulated at 6 h, but the expression of miR-136-3p was downregulated. The expression of the G1 phase-related genes CHUK, DDX24, HES1, NET1, and STAT3 was promoted by NOTCH3 upregulation, and the expression of the G0 phase-related gene CDKN1a was inhibited by NOTCH3 downregulation. These results suggested that the ceRNAs and the NOTCH3-regulated G0 phase- and G1 phase-related genes showed a correlation in expression, interaction, and role. They together regulated the hepatocytes in the G0 phase at 0 h and in the G1 phase at 6 h. These findings might help understand the mechanism by which ceRNA together regulated the hepatocytes in the G0 or G1 phase.
CSRP1 Promotes Colon Adenocarcinoma Growth and Serves as an Independent Risk Biomarker for Worse Prognosis
Background. Cysteine and Glycine Rich Protein 1 (CSRP1) belongs to the cysteine-rich protein family, which contains a unique double-zinc finger motif and is important for development and cellular differentiation. Abnormal expression of CSRP1 was reported within several malignancies such as prostate cancer and acute myeloid leukemia. Here, we explored function of CSRP1 within colon adenocarcinoma (COAD) for the first time. Methods. The mRNA levels of CSRP1 in COADs were obtained from TCGA datasets. CSRP1 protein expressions in COADs were tested via immunohistochemistry staining. Patients’ prognosis was evaluated using both univariate analysis and multivariate analysis. Two human COAD originated cancer cell lines, Caco-2, and HT-29, were used for cellular experiments including shRNA knockdown, proliferation assay, and migration assay. In vivo model was established using nude mice xenografts to further validate the role of CSRP1 in COAD progression. Results. The mRNA levels of CSRP1 are elevated in COAD specimens from patients with more advanced tumor stages and higher Carcinoembryonic Antigen (CEA) levels. In addition, higher CSRP1 mRNA level indicates worse COAD prognosis. Consistently, higher CSRP1 protein expression is correlated with worse overall survival according to both univariate and multivariate analysis, indicating that CSRP1 is a new COAD prognostic factor. Furthermore, COAD cells transfected with CSRP1-shRNAs exhibit attenuated proliferation and migration capacities. Finally, growth of xenografts originated from CSRP1-knockdown cells is inhibited comparing to the control ones. Conclusions. Expression of CSRP1 is positively correlated with COAD progression, which can promote tumor growth and migration. Higher CSRP1 can is a novel independent prognostic factor of COAD.
Immunotherapy and Immune Infiltration in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Analysis
On a global scale, renal cell carcinoma (RCC) is the second most common form of cancer and the 10th leading cause of cancer-related deaths. There are about 70% of cases of RCC that are clear cell renal cell carcinomas (ccRCCs). This study explores possible targets for immune therapy in patients with RCC. In the recent years, immunotherapy has been applied to RCC patients. In order to identify genes that are closely associated with immune cells, a weighted gene coexpression network analysis (WGCNA) was conducted. A close association was found between genes involved in MEred and M0 macrophages, M1 macrophages, and M2 macrophages. A prognostic prediction model is subsequently developed by incorporating the OS and the expression level of key genes from the RCC cohort into a univariate COX regression analysis, a multivariate COX regression analysis, and a combined COX regression analysis. We finally discovered that 6 genes are closely associated with the prognosis of RCC patients, including SLC16A12, SLC2A9, IGF2BP2, EMX2, ANK3, and METTL7A. The survival analysis proved the prognostic prediction value of the model. The 1-year, 3-year, and 5-year AUC of ROC curves are 0.759, 0.723, and 0.733, respectively. For clinical ROC curves, the AUC score for risk score, stage, grade, and T stage is 0.759, 0.824, 0722, and 0.736, respectively. The nomogram was constructed for better prognosis prediction of RCC patients. In addition, GSVA and GO enrichment analysis was performed to explore the potential pathways that are closely associated with genes involved in the prognostic prediction model. Accordingly, our study demonstrates that immune cells play a crucial role in RCC infiltration. The development of a prognostic prediction model is a potential new prognostic biomarker and potential immunotherapy target for tumors.