Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein–protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

Monkeypox (Mpox) is a major global human health threat after COVID-19. Its treatment becomes complicated with type-2 diabetes (T2D). It may happen due to the influence of both disease-causing common host key genes (cHKGs). Therefore, it is necessary to explore both disease-causing cHKGs to reveal their shared pathogenetic mechanisms and candidate drugs as their common treatments without adverse side effect. This study aimed to address these issues. At first, 3 transcriptomics datasets for each of Mpox and 6 T2D datasets were analyzed and found 52 common host differentially expressed genes (cHDEGs) that can separate both T2D and Mpox patients from the control samples. Then top-ranked six cHDEGs (HSP90AA1, B2M, IGF1R, ALD1HA1, ASS1, and HADHA) were detected as the T2D-causing cHKGs that are associated with the complexity of Mpox through the protein–protein interaction network analysis. Then common pathogenetic processes between T2D and Mpox were disclosed by cHKG-set enrichment analysis with biological processes, molecular functions, cellular components and Kyoto Encyclopedia of Genes and Genomes pathways, and regulatory network analysis with transcription factors and microRNAs. Finally, cHKG-guided top-ranked three drug molecules (tecovirimat, vindoline, and brincidofovir) were recommended as the repurposable common therapeutic agents for both Mpox and T2D by molecular docking. The absorption, distribution, metabolism, excretion, and toxicity and drug-likeness analysis of these drug molecules indicated their good pharmacokinetics properties. The 100-ns molecular dynamics simulation results (root mean square deviation, root mean square fluctuation, and molecular mechanics generalized born surface area) with the top-ranked three complexes ASS1-tecovirimat, ALDH1A1-vindoline, and B2M-brincidofovir exhibited good pharmacodynamics properties. Therefore, the results provided in this article might be important resources for diagnosis and therapies of Mpox patients who are also suffering from T2D.

Dengue fever (DF), a potentially fatal mosquito-transmitted viral disease caused by dengue virus (DENV) infections (DENVI), stands as the predominant arthropod-borne viral illness worldwide, presenting a significant global health challenge. DENV-mediated proteins/proteases interact with host proteins to develop the infection. Despite the severity of DENVI, the infection-causing host key-genes (hKGs), their pathogenetic processes, and inhibitors/activators are not yet rigorously investigated. This study aimed to disclose DENVI-causing hKGs, highlighting their pathogenetic mechanisms and therapeutic agents. At first, 115 host differentially expressed genes (hDEGs) between DENVI and control samples were identified by employing the LIMMA statistical approach. Through protein-protein interaction (PPI) network analysis, the top nine hDEGs (CDK1, BIRC5, TYMS, KIF20A, CCNB2, CDC20, AURKB, TK1, and PTEN) were detected as the infection-causing hGBs or host key-genes (hKGs). Among these hKGs, six genes (CDK1, BIRC5, TYMS, KIF20A, CCNB2, and TK1) have been emphasized as the DENVI-causing genes by the literature review. Functional enrichment analysis showed how hKGs orchestrate viral infection processes by disrupting cell cycles and immune responses. CDK1 and AURKB divert mitotic machinery to support viral replication, while PTEN and BIRC5 inhibit MAVS-MDA5 pathways to suppress interferon responses. In the nucleus, CDK1 and TYMS manipulate host transcription to favor viral processes. Key pathways identified through KEGG analysis include cell cycle and p53 signaling, explaining DENV-induced thrombocytopenia and dysregulated apoptosis. The regulatory network analysis identified five transcription factors (FOXC1, GATA2, RELA, TP53, PPARG) as the transcriptomic regulators of hKGs. The regulators FOXC1 and RELA influence EMT and inflammatory responses, and PPARG’s involvement in lipid metabolism correlates with Dengue Shock Syndrome severity, while miR-103a-3p enhances viral replication by targeting the OTUD4/p38 MAPK pathway. Finally, hKGs-guided three drug candidates (ENTRECTINIB, IMATINIB, and QL47) were selected by molecular docking analysis. These findings provide valuable insights that could significantly impact dengue fever diagnosis and treatment strategies.

Type 2 diabetes (T2D) is a crucial risk factor for both pancreatic cancer (PC) and kidney cancer (KC). However, effective common drugs for treating PC and/or KC patients who are also suffering from T2D are currently lacking, despite the probability of their co-occurrence. Taking disease-specific multiple drugs during the co-existence of multiple diseases may lead to adverse side effects or toxicity to the patients due to drug-drug interactions. This study aimed to identify T2D-, PC and KC-causing common genomic biomarkers (cGBs) highlighting their pathogenetic mechanisms to explore effective drugs as their common treatment. We analyzed transcriptomic profile datasets, applying weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis approaches to identify T2D-, PC-, and KC-causing cGBs. We then disclosed common pathogenetic mechanisms through gene ontology (GO) terms, KEGG pathways, regulatory networks, and DNA methylation of these cGBs. Initially, we identified 78 common differentially expressed genes (cDEGs) that could distinguish T2D, PC, and KC samples from controls based on their transcriptomic profiles. From these, six top-ranked cDEGs (TOP2A, BIRC5, RRM2, ALB, MUC1, and E2F7) were selected as cGBs and considered targets for exploring common drug molecules for each of three diseases. Functional enrichment analyses, including GO terms, KEGG pathways, and regulatory network analyses involving transcription factors (TFs) and microRNAs, along with DNA methylation and immune infiltration studies, revealed critical common molecular mechanisms linked to PC, KC, and T2D. Finally, we identified six top-ranked drug molecules (NVP.BHG712, Irinotecan, Olaparib, Imatinib, RG-4733, and Linsitinib) as potential common treatments for PC, KC and T2D during their co-existence, supported by the literature reviews. Thus, this bioinformatics study provides valuable insights and resources for developing a genome-guided common treatment strategy for PC and/or KC patients who are also suffering from T2D.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.