Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Background: Cervical cancer (CC) is one of the most common and serious cancers in the world. This cancer includes two histological types including Squamous cell carcinoma (SCC) and adenocarcinoma (ADC). In this project SCC have been studied. The current study aimed to identify novel potential candidate mRNA and miRNA biomarkers for CC based on a Protein-Protein Interaction (PPI) and miRNA-mRNA networks analysis.Results: In the current project, transcriptome profile for normal and CC samples was utilized. First, the PPI network was constructed for the 1335 DEGs, and then a significant gene module was extracted from the PPI network. Next, a list of miRNAs which are targeting module's genes are collected from the miRTarBase online database and a miRNA-mRNA regulatory network was formed. Afterward, CC driver genes were selected from the module's genes (MCM2, MCM10, POLA1, and TONSL) and introduced as potential candidate biomarkers for CC. As well as, two hub miRNAs including miR-193b-3p and miR-615-3p were selected from the miRNA-mRNA regulatory network and reported as potential candidate biomarkers for CC. Conclusion: In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarker for CC.

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types Squamous cell carcinoma (SCC) and adenocarcinoma (ADC).In this project, SCC has been studied. The current study aims to identify novel potential candidate mRNA and miRNA biomarkers for CC based on a Protein-Protein Interaction (PPI) and miRNA-mRNA networks analysis. The current project utilized a transcriptome profile for normal and CC samples. First, the PPI network was constructed for the 1335 DEGs, and then a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the miRTarBaseonline database, and a miRNA-mRNA regulatory network was formed. Afterward, CC driver genes were selected from the module's genes (MCM2, MCM10, POLA1, and TONSL) and introduced as potential candidate biomarkers for CC. As well as, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers for CC. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

Background: In the current generation, infertility is one of the leading reasons for relationship problems. Infertility is defined as having active intercourse without the use of birth control pills on a yearly basis not resulting in zygote development and fertilization. Methods: In the current study, genes associated to male infertility were collected, and then a PPI network was reconstructed. Next, four protein modules were discovered from the reconstructed PPI network. Afterwards, for every protein module a mRNA-miRNA interaction sub-network was drawn. Finally, based on the degree and importance of these genes, modules were introduced as the main markers of male infertility, followed by naming some drugs involved in the disease.Results: Based on PPI network analysis, five genes including TP53, ESR1, PIK3CA, BRAF, and HIF1A are introduced as key candidates in male infertility disorder. Further, hsa-miR-92a-3p, hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-335-5p, and hsa-miR-106b-5p are probably highlighted among miRNAs. Finally, a drug-gene interaction network was remade for male infertility, and CISPLATIN, DOXORUBICIN, FLUOROURACIL, DOCETAXEL, and PACLITAXEL were found to be the most potent chemotherapy drugs in inducing the illness. Conclusions: According to the findings, it is recommended that young couples, who are having chemotherapy and children, use lower risk treatments rather than the listed medicines as much as possible in their treatment. Given that the steps were computational, it is suggested that these genes and miRNAs be realistically investigated in the laboratory in future research, and it is preferable to offer alternative low-risk medicines in other studies.