Purpose The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF). Methods Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map. Results Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF. Conclusion We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

Background Colon cancer remains one of the most common malignancies across the world. Thus far, a biomarker, which can comprehensively predict the survival outcomes, clinical characteristics, and therapeutic sensitivity, is still lacking. Methods We leveraged transcriptomic data of colon cancer from the existing datasets and constructed immune-related lncRNA (irlncRNA) pairs. After integrating with clinical survival data, we performed differential analysis and identified 11 irlncRNAs signature using Lasso regression analysis. We next plotted the 1-, 5-, and 10-year curve lines of receiver operating characteristics, calculated the areas under the curve, and recognized the optimal cutoff point. Then, we validated the pair-risk model in terms of the survival outcomes of the patients involved. Moreover, we tested the reliability of the model for predicting tumor aggressiveness and therapeutic susceptibility of colon cancer. Additionally, we reemployed the 11 of irlncRNAs involved in the pair-risk model to construct an expression-risk model to predict the prognostic outcomes of the patients involved. Results We recognized a total of 377 differentially expressed irlncRNAs (DEirlcRNAs), including 28 low-expressed and 349 high-expressed irlncRNAs in colon cancer patients. After performing a univariant Cox analysis, we identified 115 risk irlncRNAs that were significantly correlated with survival outcomes of patients involved. By taking the overlap of the DEirlcRNAs and the risk irlncRNAs, we ultimately recognized 55 irlncRNAs as core irlncRNAs. Then, we established a Cox HR model (pair-risk model) as well as an expression HR model (exp-risk model) based on 11 of the 55 core irlncRNAs. We found that both of the two models significantly outperformed the commonly used clinical characteristics, including age, T, N, and M stages when predicting survival outcomes. Moreover, we validated the pair-risk model as a potential tool for studying the tumor microenvironment of colon cancer and drug susceptibility. Additionally, we noticed that combinational use of the pair-risk model and the exp-risk model yielded a more robust approach for predicting the survival outcomes of patients with colon cancer. Conclusions We recognized 11 irlncRNAs and created a pair-risk model and an exp-risk model, which have the potential to predict clinical characteristics of colon cancer, either solely or conjointly.

Background: To characterize the anatomical subtypes of ureteropelvic junction obstruction (UPJO) caused by crossing vessels (CVs) and demonstrate the Individualized operation procedures for these cases.Methods: From March 2015 to July 2019, 51 consecutive adult patients underwent treatment of primary UPJO via a retroperitoneal laparoscopic approach. The clinical data, iconography inspection results, and surgical procedures for each patient were retrospectively reviewed by our team. The diagnosis of etiological CV was confirmed during the operation in 13 patients (25.49%), which included 7 men and 6 women. Results: The mean surgical age was 30±11.66 years. The operating time was approximately 233±62.76 minutes, and there were one open conversions. In the follow-up period (mean, 27.23±15.46 months), all patients had a full recovery in the CV group. However, 3 patients without CV did not completely recover from uronephrosis, as determined on iconography inspection, and there was no improvement in the renal colic symptoms. In the CV group, none of the patients had lithiasis whereas 25% of the patients without CV had lithiasis. Conclusion: CV accounts for approximately 25.49% of the UPJO cases. Based on the anatomical position of the UPJ and CVs, we identified two types of abnormalities, and 84.62% of the CVs were located anterior to the UPJ. The retroperitoneal approach for treating CVs had particular advantages. A comprehensive understanding and interoperative analysis of the anastomosis between the CVs and UPJ is crucial for at least 4 individual treatments. After dismembered pyeloplasty, suspension of the CVs is recommended in approximately 40% of the cases. The follow-up showed good prognosis in the long term.

Histone deacetylases 1 (HDAC1), an enzyme that functions to remove acetyl molecules from ε-NH3 groups of lysine in histones, eliminates the histone acetylation at the promoter regions of tumor suppressor genes to block their expression during tumorigenesis. However, it remains unclear why HDAC1 fails to impair oncogene expression. Here we report that HDAC1 is unable to occupy at the promoters of oncogenes but maintains its occupancy with the tumor suppressors due to its interaction with CREPT (cell cycle-related and expression-elevated protein in tumor, also named RPRD1B), an oncoprotein highly expressed in tumors. We observed that CREPT competed with HDAC1 for binding to oncogene (such as CCND1, CLDN1, VEGFA, PPARD and BMP4) promoters but not the tumor suppressor gene (such as p21 and p27) promoters by a chromatin immunoprecipitation (ChIP) qPCR experiment. Using immunoprecipitation experiments, we deciphered that CREPT specifically occupied at the oncogene promoter via TCF4, a transcription factor activated by Wnt signaling. In addition, we performed a real-time quantitative PCR (qRT-PCR) analysis on cells that stably over-expressed CREPT and/or HDAC1, and we propose that HDAC1 inhibits CREPT to activate oncogene expression under Wnt signaling activation. Our findings revealed that HDAC1 functions differentially on tumor suppressors and oncogenes due to its interaction with the oncoprotein CREPT.