Search Results (671)

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

Background: Hepatocellular carcinoma (HCC) is one of the most common and fatal malignancies worldwide, characterized by remarkable molecular heterogeneity and poor clinical outcomes. Despite advancements in diagnosis and treatment, the prognosis for HCC remains dismal, largely due to late-stage diagnosis and limited therapeutic efficacy. Therefore, there is a critical need to identify novel therapeutic targets and explore alternative strategies, such as drug repurposing, to improve patient outcomes. Methods: In this study, we employed network pharmacology, molecular docking, and molecular dynamics (MD) simulations to explore the potential therapeutic targets of Nirmatrelvir in HCC. Results: Nirmatrelvir targets were predicted through SwissTarget (101 targets), SuperPred (1111 targets), and Way2Drug (38 targets). Concurrently, HCC-associated genes (5726) were retrieved from DisGeNet. Cross-referencing the two datasets identified 29 overlapping proteins. A protein–protein interaction (PPI) network constructed from the overlapping proteins was analyzed using CytoHubba, identifying 10 hub genes, with HDAC1, HDAC3, and STAT3 achieving the highest degree scores. Molecular docking revealed a strong binding affinity of Nirmatrelvir to HDAC1 (docking score = −7.319 kcal/mol), HDAC3 (−6.026 kcal/mol), and STAT3 (−6.304 kcal/mol). Moreover, Nirmatrelvir displayed stable dynamic behavior in repeated 200 ns simulation analyses. Binding free energy calculations using MM/GBSA showed values of −23.692 kcal/mol for the HDAC1–Nirmatrelvir complex, −33.360 kcal/mol for HDAC3, and −21.167 kcal/mol for STAT3. MM/PBSA analysis yielded −17.987 kcal/mol for HDAC1, −27.767 kcal/mol for HDAC3, and −16.986 kcal/mol for STAT3. Conclusions: The findings demonstrate Nirmatrelvir’s strong binding affinity towards HDAC3, underscoring its potential for future drug development. Collectively, the data provide computational evidence for repurposing Nirmatrelvir as a multi-target inhibitor in HCC therapy, warranting in vitro and in vivo studies to confirm its clinical efficacy and safety and elucidate its mechanisms of action in HCC. Full article

The midgut of Antheraea pernyi plays a critical role in antiviral defense. However, its transcriptional complexity remains poorly understood. Here, a full-length (FL) transcriptome atlas of A. pernyi midgut was developed by integrating PacBio Iso-Seq and RNA-seq techniques. The transcriptome sequences included 1850 novel protein-coding genes, 17,736 novel alternative isoforms, 1664 novel long non-coding RNAs (lncRNAs), and 858 transcription factors (TFs). In addition, 2471 alternative splicing (AS) events and 3070 alternative polyadenylation (APA) sites were identified. Moreover, 3426 and 4796 differentially expressed genes (DEGs) and isoforms were identified after ApNPV infection, respectively, besides the differentially expressed lncRNAs (164), TFs (171), and novel isoforms of ApRelish (1) and ApSOCS2 (4). Enrichment analyses showed that KEGG pathways related to metabolism were suppressed, whereas GO terms related to DNA synthesis and replication were induced. Furthermore, the autophagy and apoptosis pathways were significantly enriched among the upregulated genes. Protein–protein interaction network (PPI) analysis revealed the coordinated downregulation of genes involved in mitochondrial ribosomes, V-type and F-type ATPases, and oxidative phosphorylation, indicating the disruption of host energy metabolism and organelle acidification. Moreover, coordinated upregulation of genes associated with cytoplasmic ribosomes was observed, suggesting that the infection by ApNPV interferes with host translational machinery. These results show that ApNPV infection reprograms energy metabolism, biosynthetic processes, and immune response in A. pernyi midgut. Our study provides a foundation for elucidating the mechanisms of A. pernyi–virus interactions, particularly how the viruses affect host defense strategies. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Cinnamomum camphora is an ecologically and economically significant species, highly valued for its essential oil production and environmental benefits. Although a tissue culture system has been established for C. camphora, large-scale propagation remains limited due to the inconsistent formation of adventitious roots (ARs). This study investigated AR formation from callus tissue, focusing on associated physiological changes and gene expression dynamics. During AR induction, contents of soluble sugars and proteins decreased, alongside reduced activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and polyphenol oxidase (PPO). Levels of indole-3-acetic acid (IAA) and abscisic acid (ABA) decreased significantly throughout AR formation. Zeatin riboside (ZR) levels initially declined and then rose, whereas gibberellic acid (GA) levels displayed the opposite trend. Comparative transcriptomic and temporal expression analyses identified differentially expressed genes (DEGs), which were grouped into four distinct expression patterns. KEGG pathway enrichment indicated that 67 DEGs are involved in plant hormone signaling pathways and that 38 DEGs are involved in the starch and sucrose metabolism pathway. Additionally, protein–protein interaction network (PPI) analysis revealed ten key regulatory genes, which are mainly involved in auxin, cytokinin, GA, ABA, and ethylene signaling pathways. The reliability of the transcriptome data was further validated by quantitative real-time PCR. Overall, this study provides new insights into the physiological and molecular mechanisms underlying AR formation in C. camphora and offers valuable guidance for optimizing tissue culture systems. Full article

As a pivotal model organism in Lepidoptera research, the silkworm (Bombyx mori) holds significant importance in life science due to its economic value and biotechnological applications. Advancements in proteomics and bioinformatics have enabled substantial progress in characterizing the B. mori proteome. Systematic screening and identification of protein–protein interactions (PPIs) have progressively elucidated the molecular mechanisms governing key biological processes, including viral infection, immune regulation, and growth development. This review comprehensively summarizes traditional PPI detection techniques, such as yeast two-hybrid (Y2H) and immunoprecipitation (IP), alongside emerging methodologies such as mass spectrometry-based interactomics and artificial intelligence (AI)-driven PPI prediction. We critically analyze the strengths, limitations, and technological integration strategies for each approach, highlighting current field challenges. Furthermore, we elaborate on the molecular regulatory networks of Bombyx mori nucleopolyhedrovirus (BmNPV) from multiple perspectives: apoptosis and cell cycle regulation; viral protein invasion and trafficking; non-coding RNA-mediated modulation; metabolic reprogramming; and host immune evasion. These insights reveal the dynamic interplay between viral replication and host defense mechanisms. Collectively, this synthesis aims to provide a robust theoretical foundation and technical guidance for silkworm genetic improvement, infectious disease management, and the advancement of related biotechnological applications. Full article

Background/Objectives: Cutaneous melanoma is one of the aggressive forms of skin cancer originating from melanocytes. The high incidence of melanoma metastasis continues to rise, partly due to the complex nature of the molecular mechanisms driving its progression. While melanomas generally arise from melanocytes, we investigated whether aberrant keratinocyte differentiation pathways—like cornified envelope formation—discriminate primary melanoma from metastatic melanoma, revealing novel biomarkers in progression. Methods: In the present study, we retrieved four datasets (GSE15605, GSE46517, GSE8401, and GSE7553) associated with primary and metastatic melanoma tissues and identified differentially expressed genes (DEGs). Thereafter, an integrated meta-analysis and functional enrichment analysis of the DEGs were performed to evaluate the molecular mechanisms involved in melanoma metastasis, such as immune cell deconvolution and protein-protein interaction (PPI) network construction. Hub genes were identified based on four topological methods, including ‘Betweenness’, ‘MCC’, ‘Degree’, and ‘Bottleneck’. We validated the findings using the TCGA-SKCM cohort. Drug-gene interactions were evaluated using the DGIdb, whereas structural druggability was assessed using the ProteinPlus and AlphaFold databases. Results: We identified a total of eleven hub genes associated with melanoma progression. These included members of the keratin gene family (e.g., KRT5, KRT6A, KRT6B, etc.). Except for the gene CDH1, all the hub genes were downregulated in metastatic melanoma tissues. From a prognostic perspective, these hub genes were associated with poor prognosis (i.e., unfavorable). Using the Human Protein Atlas (HPA), immunohistochemistry evaluation revealed mostly undetected levels in metastatic melanoma. Additionally, the cornified envelope formation was the most enriched pathway, with a gene ratio of 17/33. The tumor microenvironment (TME) of metastatic melanomas was predominantly enriched in NK cell–associated signatures. Finally, several hub genes demonstrated favorable druggable potential for immunotherapy. Conclusions: Through integrated meta-analysis, this study identifies transcriptional, immunological, and structural pathways to melanoma metastasis and highlights keratin family genes as promising biomarkers for therapeutic targeting. Full article

Lycopene exhibits a broad spectrum of biological activities with potential therapeutic applications. Despite its established antioxidant and anti-inflammatory properties, the molecular basis for its pharmacological actions remains incompletely defined. Here we investigated the molecular targets, pharmacodynamic feasibility, and tissue-specific expression of lycopene targets using a computational pharmacology approach combined with affinity and protein–protein interaction (PPI) analyses. Lycopene-associated human protein targets were predicted using a Swiss target screening platform. Molecular docking was used to estimate binding affinities, and concentration-affinity (CA) ratios were calculated based on physiologically relevant plasma concentrations (75–210 nM). PPI networks of lycopene targets were constructed to identify highly connected targets, and tissue expression analysis was assessed for high-affinity targets using protein-level data from the Human Protein Atlas database. Of the 94 predicted targets, 37% were nuclear receptors and 18% were Family A G Protein Coupled Receptors (GPCRs). Among the top 15 high-affinity targets, nuclear receptors and GPCRs comprised 40% and 26.7%, respectively. Twenty targets had affinities < 10 μM, with six key targets (MAP2K2, SCN2A, SLC6A5, SCN3A, TOP2A, and TRIM24) showing submicromolar binding. CA ratio analysis identified MAP2K2, SCN2A, and SLC6A5 as pharmacodynamically feasible targets (CA > 1). PPI analysis revealed 32 targets with high interaction and 9 with significant network connectivity. Seven targets (TRIM24, GRIN1, NTRK1, FGFR1, NTRK3, CHRNB4, and PIK3CD) showed both high affinity and centrality in the interaction network. The expression profiling of submicromolar targets revealed widespread tissue distribution for MAP2K2 and SCN3A, while SCN2A, TOP2A, and TRIM24 showed more restricted expression patterns. This integrative analysis identifies a subset of lycopene targets with both high affinity and pharmacological feasibility, particularly MAP2K2, SCN2A, and TRIM24. Lycopene appears to exert its biological effects through modulation of interconnected signalling networks involving nuclear receptors, GPCRs, and ion channels. These findings support the potential of lycopene as a multi-target therapeutic agent and provide a rationale for future experimental and clinical validation. Full article

Virulence factors (VFs), produced by pathogens, facilitate pathogenic microorganisms to invade, colonize, and damage the host cells. Accurate VF identification advances pathogenic mechanism understanding and provides novel anti-virulence targets. Existing models primarily utilize protein sequence features while overlooking the systematic protein–protein interaction (PPI) information, despite pathogenesis typically resulting from coordinated protein–protein actions. Moreover, a severe imbalance exists between virulence and non-virulence proteins, which causes existing models trained on balanced datasets by sampling to fail in incorporating proteins’ inherent distributional characteristics, thus restricting generalization to real-world imbalanced data. To address these challenges, we propose a novel Generative and Contrastive self-supervised learning framework for Virulence Factor identification (GC-VF) that transforms VF identification into an imbalanced node classification task on graphs generated from PPI networks. The framework encompasses two core modules: the generative attribute reconstruction module learns attribute space representations via feature reconstruction, capturing intrinsic data patterns and reducing noise; the local contrastive learning module employs node-level contrastive learning to precisely capture local features and contextual information, avoiding global aggregation losses while ensuring node representations truly reflect inherent characteristics. Comprehensive benchmark experiments demonstrate that GC-VF outperforms baseline methods on naturally imbalanced datasets, exhibiting higher accuracy and stability, as well as providing a potential solution for accurate VF identification. Full article

Background: Diabetic foot ulcers (DFUs) are a severe complication of diabetes and are characterized by impaired wound healing and a high amputation risk. Exosomes—which are nanovesicles carrying proteins, RNAs, and lipids—mediate intercellular communication in wound microenvironments, yet their biomarker potential in DFUs remains underexplored. Methods: We analyzed transcriptomic data from GSE134431 (13 DFU vs. 8 controls) as a training set and validated findings in GSE80178 (6 DFU vs. 3 controls). A sum of 7901 differentially expressed genes (DEGs) of DFUs were detected and intersected with 125 literature-curated exosome-related genes (ERGs) to yield 51 candidates. This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). Expression trends were confirmed in both datasets. Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. Results: A nomogram combining these markers achieved an acceptable calibration (Hosmer–Lemeshow p = 0.0718, MAE = 0.044). Immune cell infiltration (CIBERSORT) revealed associations between biomarker levels and NK cell and neutrophil subsets. Gene set enrichment analysis (GSEA) implicated IL-17 signaling, proteasome function, and microbial infection pathways. A GeneMANIA network highlighted RNA processing and vesicle trafficking. Transcription factor and miRNA predictions uncovered regulatory circuits, and DGIdb-driven drug repurposing followed by molecular docking identified Indatuximab ravtansine and heparin as high-affinity SDC1 binders. Finally, RT-qPCR validation in clinical DFU tissues (n = 5) recapitulated the bioinformatic expression patterns. Conclusions: We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. These findings lay the groundwork for exosome-based diagnostics and therapeutic targeting in DFU management. Full article

Trachinotus ovatus is a euryhaline, warm-water pelagic fish species with strong adaptability, rapid growth, and a high survival rate, making it one of the most important marine aquaculture species in China. In recent years, extensive experience has been accumulated in the cage farming of T. ovatus, but whether it can adapt to deep-sea environments and grow normally remains a current research focus. This study used RNA-Seq sequencing technology to analyze the gene expression changes in the liver of T. ovatus under three conditions: rest (0 cm/s), medium flow velocity (54 cm/s), and high flow velocity (90 cm/s). Through differential expression analysis, Short Time-series Expression Miner (STEM) analysis and protein–protein interaction (PPI) network analysis, a total of 5107 differentially expressed genes (DEGs), three significantly expressed gene profiles (profile6, profile1, and profile5), and 15 hub genes were identified. The results showed that changes in flow speed significantly impacted key biological processes such as energy metabolism, protein homeostasis, and endoplasmic reticulum (ER) stress response. Under moderate and high flow conditions, glycolysis-related genes were upregulated to meet the energy demands of swimming, while the downregulation of the PPARγ-RXRG complex and its downstream genes in the lipid metabolism pathway suggested a limitation in its fatty acid β-oxidation capacity. At the same time, protein synthesis was enhanced, and the unfolded protein response (UPR) was activated to help cope with ER stress. Furthermore, when the flow speed reached 90 cm/s, the expression of UPR- related genes and the anti-apoptotic factor JNK significantly decreased, suggesting that the stress response was nearing its limit and could potentially trigger cell apoptosis. These findings provide new insights into the molecular adaptation mechanisms of T. ovatus to flow speed stress and offer theoretical support for its rational farming in deep-sea cages, suggesting that the water flow speed in farming should not exceed 90 cm/s. Full article

Background: Obesity is characterized by a chronic state of low-grade inflammation. Investigating immune-critical genes and their biological functions in the adipose tissue of postmenopausal obese women is crucial for elucidating the underlying mechanisms of immune dysregulation associated with obesity. Methods: In this study, microarray (GSE151839) and single-cell RNA-seq (GSE176171) datasets were obtained from the Gene Expression Omnibus (GEO). For microarray data analysis, weighted gene co-expression network analysis (WGCNA), protein–protein interaction network (PPI) analysis, and immune infiltration analysis (ssGSEA) were employed to identify obesity-related immune-critical genes. Subsequently, the candidate genes were validated using scRNA-seq data to explore their expression patterns at the single-cell level. Finally, the expression levels of these immune-critical genes were experimentally verified in adipose tissue from obese and control zebrafish models using RT-qPCR. Results: Analysis of microarray data through WGCNA, PPI and ssGSEA identified 16 obesity-related immune-critical genes, including IL7R, CD3E, CD2, CCR5, CD3D, MS4A1, TRAT1, SLAMF8, CCL3L1, SPP1, CCL5, IL2RG, CD3G, TLR8, ITK, and CCL3. Differential expression of SPP1, ITK and CCL5 was confirmed in scRNA-seq data, with ITK and CCL5 showing distinct expression patterns in natural killer (NK) cells. Furthermore, RT-qPCR analysis revealed upregulation of SPP1 and ITK in adipose tissue of obese zebrafish compared to lean controls. Conclusions: This study identifies SPP1, ITK and CCL5 as key immune hub genes in the adipose tissue of postmenopausal obese women, with NK cells playing a significant role in adipose tissue inflammation through the expression of these genes. These findings provide novel insights into potential therapeutic targets for the prevention and treatment of obesity in postmenopausal women. Full article

This study investigates the effect of tryptophan treatment on aged pig oocytes, focusing on its potential to reduce oxidative stress and improve oocyte quality. An oxidative stress model was induced using hydrogen peroxide (H₂O₂) to mimic aging effects on oocytes. Fresh ovaries from young sows were collected, and oocytes were aspirated and cultured for in vitro maturation. Oocytes in the H₂O₂ and the H₂O₂+Trp groups were exposed to 100 µM H₂O₂ for 30 min, with the H₂O₂+Trp group receiving an additional 50 µM tryptophan supplementation. RNA-sequencing was performed to study the underlying mechanism through which tryptophan mitigated the H₂O₂-induced oxidative stress in oocytes. The results demonstrated that tryptophan supplementation significantly reduced oxidative stress markers such as H₂O₂ and malonaldehyde (MDA) while restoring key antioxidant enzymes such as superoxide dismutase (SOD), and catalase (CAT) confirming its antioxidant role. Furthermore, tryptophan improved cumulus cell expansion, and oocyte quality, which were compromised by oxidative stress. Transcriptomics study revealed the enrichment of several KEGG pathways, such as P13K-Akt signaling pathways as a critical regulator of cell survival and function, emphasizing the protective effects of tryptophan on oocyte integrity. Moreover, the protein–protein interaction (PPI) network identified several hub genes in the tryptophan-treated group compared with H₂O₂, including TIMP1, CCN2, and MMP12 as key players in ECM remodeling and cellular adhesion, which are critical for restoring oocyte quality. These findings suggest that tryptophan supplementation not only mitigated oxidative stress but also modulated gene expression related to cellular functions and stress response. These results propose that tryptophan could be a valuable therapeutic strategy for improving reproductive outcomes in aging sows and other mammals facing age-related oocyte dysfunction. Full article

Background: Rheumatoid arthritis-related interstitial lung disease (RA-ILD) is a significant complication of RA which lacks effective treatments with high mortality. This study aimed to investigate the role of matrix metalloproteinase-7 (MMP-7) in mediating RA-ILD. Methods: Based on the database of RA-ILD patients, a bioinformatics analysis was performed. A protein–protein interaction (PPI) network focusing on MMP-7 was simulated. Pleural mesothelial cells (PMCs) were treated with RA-ILD patients’ serum or RA-ILD-related inflammatory factors, and the protein expressions of collagen-I and MMP-7 were examined. An arthritis model was established using complete Freund’s adjuvant (CFA). Changes in the weight and joints of mice were recorded, and lung tissues were evaluated by Masson staining and Sirius red stain techniques. MMP-7 inhibitor, MMP-7 siRNA and MMP shRNA lentivirus were used to inhibit MMP-7 and investigate changes in collagen-I and fibrosis in vivo and in vitro. Results: MMP-7 was found to be significantly expressed in RA-ILD lung tissue by bioinformatics analysis, and MMP-7 to maybe interact with collagen-I. In vitro experiments indicated cytokines IL-1β, IL-6 and TNF-α promoted MMP-7 and collagen-I expression in PMCs. Serum obtained from patients with RA-ILD also upregulated MMP-7 and collagen-I expression in PMCs. Inhibition of MMP-7 with MMP-7 siRNA or MMP inhibitor prevented collagen-I synthesis in PMCs. In vivo, CFA induced arthritis and subpleural lung inflammation in rats, but the MMP-7 inhibitor and MMP-7 siRNA attenuated CFA-induced lung inflammation and subpleural lung fibrosis. Conclusions: MMP-7 mediated subpleural lung inflammation as well as fibrosis in RA-ILD. It provided theoretical and experimental support for MMP-7 being a therapeutic target in RA-ILD. Full article

Currently, there is no standard treatment for renal cell carcinoma (RCC) that is free of side effects and resistance. Additionally, limited information exists on how curcumin affects the gene expression profiles of patients with translocation renal cell carcinoma (tRCC) and papillary renal cell carcinoma (pRCC). The pathways responsible for metastasis in tRCC are still not well understood, and there is no established treatment or reliable biomarker to predict outcomes for metastatic tRCC. Primary clinical data from patients were retrieved from the TCGA database and analyzed using cBioPortal, stitch, string, R and Python. Various analyses were performed, including differential gene expression, protein-protein interaction (PPI) network analysis, drug-targeted gene analysis, gene ontology (GO), enrichment analyses, and systematic searches to assess the impact of curcumin on the transcriptomic profiles of tRCC, pRCC, and clear cell renal cell carcinoma (ccRCC). No significant impact of sensitive genes on survival in KIRC and KIRP was found, though a trend suggested they may delay disease progression. The combination of curcumin with sunitinib showed promise in overcoming drug resistance in ccRCC by inducing ferroptosis, reducing iron, and increasing ADAMTS18 expression. This study, leveraging data from the TCGA database and other databases explored the impact of curcumin on transcriptomic profiles in tRCC, pRCC, and clear cell RCC (ccRCC). Gene analysis revealed immune and metabolic differences, with KIRC showing a stronger immune response. This study is the first to propose that future research into the miR-148/ADAMTS18 genes and the ferroptosis pathway in tRCC and pRCC could lead to the development of new therapies and the identification of novel therapeutic targets, potentially overcoming drug resistance and metastasis. Full article