Diabetic nephropathy (DN) is specified by microalbuminuria, glomerular lesions, and renal fibrosis leading to end-stage renal disease. The pathophysiology of DN is multifactorial as a result of gene–environment interaction. Clinical studies suggested that gene mutations affect various pathways involved in DN, including extracellular matrix (ECM). During chronic hyperglycemia, collagen type-4-mediated ECM overproduction occurs, leading to renal fibrosis and DN development. In this study, COL4A1 gene variant rs605143 (G/A) was analyzed in diabetes and DN patients from the study population. We genotyped 386 study subjects, comprising 120 type 2 diabetes mellitus (T2DM) patients, 120 DN, and 146 healthy controls. All study subjects were analyzed for biochemical assays by commercially available kits and genotypic analysis by polymerase chain reaction–restriction fragment length polymorphism and confirmed by Sanger sequencing. Statistical analyses were done using SPSS and GraphPad. Anthroclinicopathological parameters showed a significant association between T2DM and DN. Genotype AA of COL4A1 gene variant rs605143 (G/A) showed a significant association with T2DM and DN compared with controls with 5.87- and 8.01-folds risk, respectively. Mutant allele A also significantly associated with T2DM and DN independently compared with healthy controls with 2.29- and 2.81-time risk in the study population. This study's findings suggested that COL4A1 gene variant rs605143 (G/A) can be used as predictive biomarkers for T2DM and DN independently. However, this gene variant needs to be analyzed in a large sample to explore the shared genetic association between T2DM and DN.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with significant resistance to conventional therapies, highlighting the urgent need for novel therapeutic strategies. Moringa oleifera (M. oleifera), a medicinal plant rich in diverse bioactive compounds, has shown promising potential for anti-lung carcinoma activity. This study investigates the molecular mechanisms underlying the therapeutic effects of M. oleifera bioactive compounds for their anti-lung cancer activities through an integrated network modeling and molecular docking approach. By constructing comprehensive compound–target–lung cancer pathway networks, we aim to elucidate the multitarget pharmacology of M. oleifera compounds, providing valuable insights into their potential as therapeutic candidates. Computational pipeline was applied to identify 180 phytochemicals from M. oleifera, filtered using Lipinski’s Rule of Five and ADMET properties, resulting in 10 lead compounds followed by their potential biological target proteins in regulating lung cancer progression. We identified 80 targeted proteins involved in lung cancer, with EGFR being the most enriched in pathway enrichment analysis. In the molecular docking analysis, caffeic acid showed the highest binding score (−28.97 kcal/mol) with EGFR forming stable complex during molecular dynamics simulations compared to the known EGFR inhibitor ‘erlotinib’. The overall results suggest that caffeic acid, a key bioactive compound in M. oleifera, is an EGFR-mediated oncogenic signaling inhibitor for lung cancer therapy, warranting further experimental validation to translate these findings into clinical applications.