Bioinformatics analysis and identification of potential genes related to pathogenesis of cervical intraepithelial neoplasia

An integrative approach toward identification and analysis of therapeutic targets involved in HPV pathogenesis with a focus on carcinomas

BACKGROUND

Persistent infection of high-risk HPVs is known to cause diverse carcinomas, mainly cervical, oropharyngeal, penile, etc. However, efficient treatment is still lacking.

OBJECTIVE

Identify and analyze potential therapeutic targets involved in HPV oncogenesis and repurposing drug candidates.

METHODS

Integrative analyses were performed on the compendium of 1887 HPV infection-associated or integration-driven disrupted genes cataloged from the Open Targets Platform and HPVbase resource. Potential target genes are prioritized using STRING, Cytoscape, cytoHubba, and MCODE. Gene ontology and KEGG pathway enrichment analysis are performed. Further, TCGA cancer genomic data of CESC and HNSCC is analyzed. Moreover, regulatory networks are also deduced by employing NetworkAnalyst.

RESULTS

We have implemented a unique approach for identifying and prioritizing druggable targets and repurposing drug candidates against HPV oncogenesis. Overall, hundred key genes with 44 core targets were prioritized with transcription factors (TFs) and microRNAs (miRNAs) regulators pertinent to HPV pathogenesis. Genomic alteration profiling further substantiated our findings. Among identified druggable targets, TP53, NOTCH1, PIK3CA, EP300, CREBBP, EGFR, ERBB2, PTEN, and FN1 are frequently mutated in CESC and HNSCC. Furthermore, PIK3CA, CCND1, RFC4, KAT5, MYC, PTK2, EGFR, and ERBB2 show significant copy number gain, and FN1, CHEK1, CUL1, EZH2, NRAS, and H2AFX was marked for the substantial copy number loss in both carcinomas. Likewise, under-explored relevant regulators, i.e., TFs (HINFP, ARID3A, NFATC2, NKX3-2, EN1) and miRNAs (has-mir-98-5p, has-mir-24-3p, has-mir-192-5p, has-mir-519d-3p) is also identified.

CONCLUSIONS

We have identified potential therapeutic targets, transcriptional and post-transcriptional regulators to explicate HPV pathogenesis as well as potential repurposing drug candidates. This study would aid in biomarker and drug discovery against HPV-mediated carcinoma.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Objective

Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.

Methods

Five-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.

Results

The DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.

Conclusion

We developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

A regulatory miRNA-mRNA network is associated with transplantation response in acute kidney injury

Background

Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA–mRNA network.

Results

Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA–mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA–mRNA network by Cytoscape. The miRNA–mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K–Akt/HIF-1/Ras/MAPK signaling pathways.

Conclusion

We constructed a regulatory miRNA–mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00363-y.

Maintaining the Balance of Intestinal Flora through the Diet: Effective Prevention of Illness

The human body is home to a complex community of dynamic equilibrium microbiota, including bacteria, fungi, parasites, and viruses. It is known that the gut microbiome plays a crucial role in regulating innate and adaptive immune responses, intestinal peristalsis, intestinal barrier homeostasis, nutrient uptake, and fat distribution. The complex relationship between the host and microbiome suggests that when this relationship is out of balance, the microbiome may contribute to disease development. The brain-gut-microbial axis is composed of many signal molecules, gastrointestinal mucosal cells, the vagus nerve, and blood-brain barrier, which plays an essential role in developing many diseases. The microbiome can influence the central nervous system function through the brain-gut axis; the central nervous system can also affect the composition and partial functions of the gut microbiome in the same way. Different dietary patterns, specific dietary components, and functional dietary factors can significantly affect intestinal flora's structure, composition, and function, thereby affecting human health. Based on the above, this paper reviewed the relationship between diet, intestinal flora, and human health, and the strategies to prevent mental illness through the dietary modification of intestinal microorganisms.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.