Sahin, T. K., Rizzo, A., Aksoy, S. & Guven, D. C. Prognostic significance of the royal marsden hospital (RMH) score in patients with cancer: A systematic review and meta-analysis. Cancers (Basel). 16, 63 (2024).Article
Google Scholar
Rizzo, A. et al. Hypertransaminasemia in cancer patients receiving immunotherapy and immune-based combinations: The MOUSEION-05 study. Cancer Immunol. Immunother. 72, 1381–1394 (2023).Article
PubMed
PubMed Central
Google Scholar
Barth, D. A. et al. Current concepts of non-coding RNAs in the pathogenesis of non-clear cell renal cell carcinoma. Cancers (Basel). 11, 21 (2019).Article
ADS
Google Scholar
Kusmartsev, S. Acute kidney injury–induced systemic inflammation and risk of kidney cancer formation. Cancer Res. 81, 2584–2585 (2021).Article
PubMed
Google Scholar
Sung, W. W., Ko, P. Y., Chen, W. J., Wang, S. C. & Chen, S. L. Trends in the kidney cancer mortality-to-incidence ratios according to health care expenditures of 56 countries. Sci. Rep. 11, 638 (2021).Article
Google Scholar
Bahadoram, S. et al. Renal cell carcinoma: An overview of the epidemiology, diagnosis, and treatment. G. Ital. di Nefrol. 39, 32–47 (2022).
Google Scholar
Mancini, M., Righetto, M. & Baggio, G. Gender-related approach to kidney cancer management: Moving forward. Int. J. Mol. Sci. 21, 638 (2020).Article
Google Scholar
DeFronzo, R. A. et al. Type 2 diabetes mellitus. Nat. Rev. Dis. Prim. 1, 1–23 (2015).
Google Scholar
Duan, X., Wang, W., Pan, Q. & Guo, L. Type 2 diabetes mellitus intersects with pancreatic cancer diagnosis and development. Front. Oncol. 11, 258 (2021).Article
Google Scholar
Zhang, P. H. et al. Increased risk of cancer in patients with type 2 diabetes mellitus: A retrospective cohort study in China. BMC Public Health 12, 1 (2012).Article
MathSciNet
Google Scholar
Tseng, C. H. Type 2 diabetes mellitus and kidney cancer risk: A retrospective cohort analysis of the National Health Insurance. PLoS One 10, 1–14 (2015).Article
Google Scholar
Habib, S. L., Prihoda, T. J., Luna, M. & Werner, S. A. Diabetes and risk of renal cell carcinoma. J. Cancer 3, 42–48 (2012).Article
PubMed
Google Scholar
A., V. et al. Pre-existing type 2 diabetes mellitus is an independent risk factor for mortality and progression in patients with renal cell carcinoma. Med. (United States) 93, no pagination (2014).Li, Y., Hu, L., Xia, Q., Yuan, Y. & Mi, Y. The impact of metformin use on survival in kidney cancer patients with diabetes: A meta-analysis. Int. Urol. Nephrol. 49, 975–981 (2017).Article
PubMed
PubMed Central
Google Scholar
Ouzaid, I. Kidney cancer. Prog. en Urol. FMC 26, F44–F45 (2016).Article
Google Scholar
Guraya, S. Y. Association of type 2 diabetes mellitus and the risk of colorectal cancer: A meta-analysis and systematic review. World J. Gastroenterol. 21, 6026–6031 (2015).Article
PubMed
PubMed Central
Google Scholar
Qi, X., Li, Q., Che, X., Wang, Q. & Wu, G. The uniqueness of clear cell renal cell carcinoma: summary of the process and abnormality of glucose metabolism and lipid metabolism in ccRCC. Front. Oncol. 11, 63 (2021).Article
Google Scholar
Rahman, M. R. et al. Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis. Brief. Bioinform. 22, 96 (2021).Article
Google Scholar
Rysz, J., Franczyk, B., Ławiński, J., Olszewski, R. & Gluba-Brzózka, A. The role of metabolic factors in renal cancers. Int. J. Mol. Sci. 21, 1–20 (2020).Article
Google Scholar
Cheng, X. & Hou, Y. Importance of metabolic and immune profile as a prognostic indicator in patients with diabetic clear cell renal cell carcinoma. Front. Oncol. 13, 214 (2023).Article
Google Scholar
Rahman, I., Athar, M. T. & Islam, M. Type 2 diabetes, obesity, and cancer share some common and critical pathways. Front. Oncol. 10, 1–10 (2021).Article
Google Scholar
Lucke, T. et al. Disease-specific medication, patient-reported diagnoses and their relation to COPD severity for common comorbidities in COPD. PA698 (2017) https://doi.org/10.1183/1393003.congress-2017.pa698.Zhang, P., Wang, F., Hu, J. & Sorrentino, R. Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep. 5, 21 (2015).
Google Scholar
Sommer, J., Seeling, A. & Rupprecht, H. Adverse drug events in patients with chronic kidney disease associated with multiple drug interactions and polypharmacy. Drugs Aging 37, 359–372 (2020).Article
PubMed
Google Scholar
Merel, S. E. & Paauw, D. S. Common drug side effects and drug-drug interactions in elderly adults in primary care. J. Am. Geriatr. Soc. 65, 1578–1585 (2017).Article
PubMed
Google Scholar
Borchelt, M. [Potential side-effects and interactions in multiple medication in elderly patients: methodology and results of the Berlin Study of Aging]. Zeitschrift Für Gerontol. Und Geriatr. Organ Der Dtsch. Gesellschaft Für Gerontol. Und Geriatr. 28, 420–428 (1995).Wang, X. et al. The potential mechanism of Guizhi Fuling Wan effect in the treatment of cervical squamous cell carcinoma: A bioinformatics analysis investigation. Med. (United States) 103, E37153 (2024).Wang, T., Jiang, X., Ruan, Y., Li, L. & Chu, L. The mechanism of action of the combination of Astragalus membranaceus and Ligusticum chuanxiong in the treatment of ischemic stroke based on network pharmacology and molecular docking. Med. (United States) 101, (2022).Jiang, X. et al. Exploration of Fuzheng Yugan Mixture on COVID-19 based on network pharmacology and molecular docking. Med. (United States) 102, E32693 (2023).Islam, M. A. et al. Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing. Sci. Rep. 13, 4685 (2023).Article
ADS
PubMed
PubMed Central
Google Scholar
Hossen, M. B. et al. Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer. Comput. Biol. Med. 152, 106411 (2023).Article
PubMed
Google Scholar
Chang, H. T. Biomarker discovery using dry-lab technologies and high-throughput screening. Biomark. Med. 10, 559–561 (2016).Article
ADS
PubMed
Google Scholar
Zhu, H. et al. Gene expression profiling of type 2 diabetes mellitus by bioinformatics analysis. Comput. Math. Methods Med. 2020, 536 (2020).Article
ADS
Google Scholar
Ding, L., Fan, L., Xu, X., Fu, J. & Xue, Y. Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis. Mol. Med. Rep. 20, 2597–2608 (2019).PubMed
PubMed Central
Google Scholar
Dong, Z. et al. Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis. Adipocyte 10, 310–321 (2021).Article
PubMed
PubMed Central
Google Scholar
Miao, S. et al. Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma. Med. Oncol. 39, 56 (2022).Article
Google Scholar
Huang, H. et al. Identification of hub genes associated with clear cell renal cell carcinoma by integrated bioinformatics analysis. Front. Oncol. 11, 14 (2021).
Google Scholar
Wang, J. et al. Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma. Oncol. Lett. 15, 9133–9141 (2018).PubMed
PubMed Central
Google Scholar
Saito, T. et al. Metformin, a diabetes drug, eliminates tumor-initiating hepatocellular carcinoma cells. PLoS One 8, 1–11 (2013).Article
Google Scholar
Shi, T., Kobara, H., Oura, K. & Masaki, T. Mechanisms underlying hepatocellular carcinoma progression in patients with type 2 diabetes. J. Hepatocell. Carcinoma 8, 45–55 (2021).Article
PubMed
PubMed Central
Google Scholar
Peng, W. F. et al. The key genes underlying pathophysiology association between the type 2-diabetic and colorectal cancer. J. Cell. Physiol. 233, 8551–8557 (2018).Article
PubMed
Google Scholar
Liu, X. et al. Identification of the shared gene signature and biological mechanism between type 2 diabetes and colorectal cancer. Front. Genet. 14, 169 (2023).Article
Google Scholar
Song, Y. et al. Bulk and single-cell transcriptome analyses of islet tissue unravel gene signatures associated with pyroptosis and immune infiltration in type 2 diabetes. Front. Endocrinol. (Lausanne). 14, (2023).Liu, S. et al. Downregulation of ALDH6A1 is a new marker of muscle insulin resistance in type 2 diabetes mellitus. Int. J. Gen. Med. 15, 2137–2147 (2022).Article
PubMed
PubMed Central
Google Scholar
Yang, T. et al. Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing. Eur. J. Med. Res. 28, 452 (2023).Article
Google Scholar
Tong, X. et al. Screening and validation of differentially expressed genes in adipose tissue of patients with obesity and type 2 diabetes mellitus. Biomol. Biomed. 24, 40–50 (2024).PubMed
PubMed Central
Google Scholar
Han, M. et al. Identification of biomarkers and construction of a microRNA-mRNA regulatory network for clear cell renal cell carcinoma using integrated bioinformatics analysis. PLoS One 16, 21 (2021).
Google Scholar
Liu, B. et al. Identification and verification of biomarker in clear cell renal cell carcinoma via bioinformatics and neural network model. Biomed Res. Int. 2020, 1463 (2020).
Google Scholar
Heng, B. et al. PIWI-interacting RNA pathway genes: potential biomarkers for clear cell renal cell carcinoma. Dis. Markers 2022, 140 (2022).Article
Google Scholar
Xu, S. et al. G Protein γ subunit 7 loss contributes to progression of clear cell renal cell carcinoma. J. Cell. Physiol. 234, 20002–20012 (2019).Article
PubMed
PubMed Central
Google Scholar
Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 210 (2004).Article
ADS
MathSciNet
Google Scholar
Alam, M. S. et al. Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies. PLoS One 17, 14639 (2022).Article
Google Scholar
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).Article
PubMed
PubMed Central
Google Scholar
Ben-Hur, A. & Noble, W. S. Kernel methods for predicting protein-protein interactions. Bioinformatics 21, 718 (2005).Article
Google Scholar
Wang, T., Jiang, X., Lu, Y., Ruan, Y. & Wang, J. Identification and integration analysis of a novel prognostic signature associated with cuproptosis-related ferroptosis genes and relevant lncRNA regulatory axis in lung adenocarcinoma. Aging (Albany. NY). 15, 1543–1563 (2023).Szklarczyk, D. et al. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, 245 (2011).Article
Google Scholar
Freeman, L. C. A set of measures of centrality based on betweenness. Sociometry 40, 639 (1977).Article
Google Scholar
Jeong, H., Mason, S. P., Barabási, A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 930 (2001).Article
Google Scholar
Urban, W. & Rogowska, P. The case study of bottlenecks identification for practical implementation to the theory of constraints. Multidiscip. Asp. Prod. Eng. 1, 399–405 (2018).
Google Scholar
Saxena, A., Gera, R. & Iyengar, S. R. S. A faster method to estimate closeness centrality ranking. 1–25 (2017).McDonnell, A., Lavelle, J., Gunnigle, P. & Collings, D. G. Management research on multinational corporations: A methodological critique. Econ. Soc. Rev. (Irel) 38, 235–258 (2007).
Google Scholar
Lavorato, M., Franco, J. F., Rider, M. J. & Romero, R. Imposing radiality constraints in distribution system optimization problems. IEEE Trans. Power Syst. 27, 172–180 (2012).Article
ADS
Google Scholar
Franssen, J. M., Cowez, B. & Gernay, T. Effective stress method to be used in beam finite elements to take local instabilities into account. Fire Saf. Sci. 11, 544–557 (2014).Article
Google Scholar
Paul, S. et al. Cytoscape: A software environment for integrated models. Genome Res. 13, 426 (1971).
Google Scholar
Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, 145 (2019).Article
Google Scholar
Zhou, W. et al. Identification of key genes involved in pancreatic ductal adenocarcinoma with diabetes mellitus based on gene expression profiling analysis. Pathol. Oncol. Res. 27, 16 (2021).Article
Google Scholar
Zhang, X., Gao, L., Liu, Z. P. & Chen, L. Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity. BMC Bioinformatics 16, 210 (2015).Article
Google Scholar
Castro-Mondragon, J. A. et al. JASPAR 2022: The 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022).Article
PubMed
Google Scholar
Xia, J., Gill, E. E. & Hancock, R. E. W. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10, 416 (2015).Article
Google Scholar
Sethupathy, P., Corda, B. & Hatzigeorgiou, A. G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 12, 1963 (2006).Article
Google Scholar
Boyle, E. I. et al. GO::TermFinder-Open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20, 3710–3715 (2004).Article
PubMed
Google Scholar
Dennis, G. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4, 2146 (2003).Article
Google Scholar
Dennis, G. Jr. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4(5), 3 (2003).Article
Google Scholar
Modhukur, V. et al. MethSurv: A web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics 10, 277–288 (2018).Article
PubMed
Google Scholar
Chandrashekar, D. S. et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia (United States) 19, (2017).Reza, M. S. et al. Bioinformatics screening of potential biomarkers from mRNA expression profiles to discover drug targets and agents for cervical cancer. Int. J. Mol. Sci. 23, 693 (2022).Article
Google Scholar
Ali, M. A. et al. Exploring the therapeutic potential of Petiveria alliacea L. phytochemicals: A computational study on inhibiting SARS-CoV-2’s main protease (Mpro). Molecules 29, 6934 (2024).Article
Google Scholar
Wang, T. et al. Exploring the mechanism of luteolin by regulating microglia polarization based on network pharmacology and in vitro experiments. Sci. Rep. 13, 2145 (2023).
Google Scholar
Wang, T. et al. Prediction and validation of potential molecular targets for the combination of Astragalus membranaceus and Angelica sinensis in the treatment of atherosclerosis based on network pharmacology. Med. (United States) 101, E29762 (2022).Wang, T., Jiang, X., Ruan, Y., Zhuang, J. & Yin, Y. Based on network pharmacology and in vitro experiments to prove the effective inhibition of myocardial fibrosis by Buyang Huanwu decoction. Bioengineered 13, 13767–13783 (2022).Article
PubMed
PubMed Central
Google Scholar
Saur, I. M. L., Panstruga, R. & Schulze-Lefert, P. NOD-like receptor-mediated plant immunity: from structure to cell death. Nat. Rev. Immunol. 21, 305–318 (2021).Article
PubMed
Google Scholar
Waterhouse, A. et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 46, 69 (2018).Article
Google Scholar
Dallakyan, S. & Olson, A. Participation in global governance: Coordinating ‘the voices of those most affected by food insecurity’. Glob. Food Secur. Gov. 1263, 1–11 (2015).
Google Scholar
Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. NA-NA https://doi.org/10.1002/jcc.21334 (2009).Article
Google Scholar
Kim, S. et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019).Article
PubMed
Google Scholar
Adasme, M. F. et al. PLIP 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res. 49, W530–W534 (2021).Article
PubMed
PubMed Central
Google Scholar
Rauf, M. A., Zubair, S. & Azhar, A. Ligand docking and binding site analysis with pymol and autodock/vina. Int. J. Basic Appl. Sci. 4, 168 (2015).Article
Google Scholar
Shaweta, S., Akhil, S. & Utsav, G. Molecular Docking studies on the Anti-fungal activity of Allium sativum (Garlic) against Mucormycosis (black fungus) by BIOVIA discovery studio visualizer 21.1.0.0. Ann. Antivirals Antiretrovir. 028–032 (2021) https://doi.org/10.17352/aaa.000013.Lipinski, C. A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today Technol. 1, 337–341 (2004).Article
PubMed
Google Scholar
Daina, A., Michielin, O. & Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 960 (2017).Article
Google Scholar
Pires, Blundell, T. L. & Ascher, D. B. pkCSM : predicting small-molecule pharmacokinetic properties using graph-based signatures (Theory- How to Enterpret pkCSM Result). pKCSM 5 (2015).Alshammari, E., Zhang, Y., Sobota, J. & Yang, Z. Aberrant DNA methylation of tumor suppressor genes and oncogenes as cancer biomarkers. Genomic Epigenomic Biomarkers Toxicol. Dis. 6, 251–271 (2022).Article
Google Scholar
Wessel, M. D., Jurs, P. C., Tolan, J. W. & Muskal, S. M. Prediction of human intestinal absorption of drug compounds from molecular structure. J. Chem. Inf. Comput. Sci. 38, 726–735 (1998).Article
PubMed
Google Scholar
Pires, D. E. V., Blundell, T. L. & Ascher, D. B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 58, 4066–4072 (2015).Article
PubMed
PubMed Central
Google Scholar
Zhao, M. et al. Cytochrome p450 enzymes and drug metabolism in humans. Int. J. Mol. Sci. 22, 96 (2021).Article
ADS
Google Scholar
Graff, R. E. et al. Type 2 diabetes in relation to the risk of renal cell carcinoma among men and women in two large prospective cohort studies. Diabetes Care 41, 1432–1437 (2018).Article
PubMed
PubMed Central
Google Scholar
Joh, H. K., Willett, W. C. & Cho, E. Type 2 diabetes and the risk of renal cell cancer in women. Diabetes Care 34, 1552–1556 (2011).Article
PubMed
PubMed Central
Google Scholar
Wang, K., Sun, Y., Tao, W., Fei, X. & Chang, C. Androgen receptor (AR) promotes clear cell renal cell carcinoma (ccRCC) migration and invasion via altering the circHIAT1/miR-195-5p/29a-3p/29c-3p/CDC42 signals. Cancer Lett. 394, 1–12 (2017).Article
ADS
PubMed
Google Scholar
Huang, Q. Y. et al. Cdc42: A novel regulator of insulin secretion and diabetes-associated diseases. Int. J. Mol. Sci. 20, 13 (2019).
Google Scholar
Wang, X. et al. Association of cluster determinant 36, scavenger receptor class B type 1, and major facilitator superfamily domain containing the 2a genetic polymorphism with serum lipid profile in aging population with type 2 diabetes mellitus. Front. Nutr. 9, 2196 (2022).
Google Scholar
Wamique, M., Himanshu, D. & Ali, W. Expression levels and genetic polymorphism of scavenger receptor class B type 1 as a biomarker of type 2 diabetes mellitus. Sultan Qaboos Univ. Med. J. 22, 117–122 (2022).Article
PubMed
PubMed Central
Google Scholar
Song, L. et al. Bridging the gap between clear cell renal cell carcinoma and cutaneous melanoma: the role of SCARB1 in dysregulated cholesterol metabolism. Aging (Albany. NY). 15, 10370–10388 (2023).Afzal, M. et al. Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer’s disease. Heliyon 9, 11 (2023).Article
Google Scholar
Vastrad, B., Tengli, A., Vastrad, C. & Kotturshetti, I. Bioinformatics analysis of key genes and pathways for obesity associated type 2 diabetes mellitus as a therapeutic target. bioRxiv (2020) https://doi.org/10.1101/2020.12.25.424383.Hua, S. et al. Identification and validation of an immune-related gene prognostic signature for clear cell renal carcinoma. Front. Immunol. 13, 639 (2022).Article
Google Scholar
de Moraes, R. J. et al. Gain-of-function SNPs in NLRP3 and IL1B genes confer protection against obesity and T2D: Undiscovered role of inflammasome genetics in metabolic homeostasis?. Endocrine 60, 368–371 (2018).Article
Google Scholar
Wang, F. et al. IL1 genes polymorphism and the risk of renal cell carcinoma in Chinese Han population. Oncotarget 8, 56021–56029 (2017).Article
PubMed
PubMed Central
Google Scholar
Chittezhath, M. et al. Molecular profiling reveals a tumor-promoting phenotype of monocytes and macrophages in human cancer progression. Immunity 41, 815–829 (2014).Article
PubMed
Google Scholar
Buraczynska, M., Ksiazek, K., Wacinski, P. & Zaluska, W. Interleukin-1β gene (IL1B) polymorphism and risk of developing diabetic nephropathy. Immunol. Invest. 48, 577–584 (2019).Article
PubMed
Google Scholar
Fan, Y. et al. Comparison of kidney transcriptomic profiles of early and advanced diabetic nephropathy reveals potential new mechanisms for disease progression. Diabetes 68, 2301–2314 (2019).Article
PubMed
PubMed Central
Google Scholar
Ferreira, W. A. S. & de Oliveira, E. H. C. Expression of GOT2 Is epigenetically regulated by DNA methylation and correlates with immune infiltrates in clear-cell renal cell carcinoma. Curr. Issues Mol. Biol. 44, 2472–2489 (2022).Article
PubMed
Google Scholar
Hussey, S. E. et al. Effect of exercise on the skeletal muscle proteome in patients with type 2 diabetes. Med. Sci. Sports Exerc. 45, 1069–1076 (2013).Article
PubMed
PubMed Central
Google Scholar
Yang, R. & Trevillyan, J. M. c-Jun N-terminal kinase pathways in diabetes. Int. J. Biochem. Cell Biol. 40, 2702–2706 (2008).Article
PubMed
Google Scholar
Oya, M. et al. c-Jun activation in acquired cystic kidney disease and renal cell carcinoma. J. Urol. 174, 726–730 (2005).Article
PubMed
Google Scholar
Huang, N. Q. et al. TLR4 is a link between diabetes and Alzheimer’s disease. Behav. Brain Res. 316, 234–244 (2017).Article
PubMed
Google Scholar
Manolakis, A. C. et al. TLR4 gene polymorphisms: Evidence for protection against type 2 diabetes but not for diabetes-associated ischaemic heart disease. Eur. J. Endocrinol. 165, 261–267 (2011).Article
PubMed
Google Scholar
Yuan, S. et al. The role of TLR4 on PGC-1α-mediated oxidative stress in tubular cell in diabetic kidney disease. Oxid. Med. Cell. Longev. 2018, 1593 (2018).Article
Google Scholar
Tang, S. C. W. & Yiu, W. H. Innate immunity in diabetic kidney disease. Nat. Rev. Nephrol. 16, 206–222 (2020).Article
PubMed
Google Scholar
Wang, Y., Zhao, M. & Zhang, Y. Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis. Bioengineered 12, 5386–5401 (2021).Article
PubMed
PubMed Central
Google Scholar
Dong, Y. et al. Identification of C3 and FN1 as potential biomarkers associated with progression and prognosis for clear cell renal cell carcinoma. BMC Cancer 21, 140 (2021).Article
Google Scholar
Steffens, S. et al. Fibronectin 1 protein expression in clear cell renal cell carcinoma. Oncol. Lett. 3, 787–790 (2012).PubMed
PubMed Central
Google Scholar
Wifi, M. N. A., Assem, M., Elsherif, R. H., El-Azab, H. A. F. & Saif, A. Toll-like receptors-2 and -9 (TLR2 and TLR9) gene polymorphism in patients with type 2 diabetes and diabetic foot. Med. (United States) 96, (2017).Castoldi, A. et al. TLR2, TLR4 and the Myd88 signaling pathway are crucial for neutrophil migration in acute kidney injury induced by sepsis. PLoS One 7, 5968 (2012).Article
Google Scholar
Li, F. et al. Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma. Comput. Biol. Chem. 92, 107453 (2021).Article
PubMed
Google Scholar
da Silva, B. R. et al. Functional haplotype in the Interleukin8 (CXCL8) gene is associated with type 2 Diabetes Mellitus and Periodontitis in Brazilian population. Diabetes Metab. Syndr. Clin. Res. Rev. 14, 1665–1672 (2020).Article
Google Scholar
Higurashi, M. et al. Increased urinary levels of CXCL5, CXCL8 and CXCL9 in patients with Type 2 diabetic nephropathy. J. Diabetes Complications 23, 178–184 (2009).Article
PubMed
Google Scholar
Abakumova, T., Myagdieva, I., Dolgova, D., Gorshkov, O. & Gening, T. Annals of oncology. Ann. Oncol. 33, S1422 (2022).Article
Google Scholar
Russo, R. C., Garcia, C. C., Teixeira, M. M. & Amaral, F. A. The CXCL8/IL-8 chemokine family and its receptors in inflammatory diseases. Expert Rev. Clin. Immunol. 10, 593–619 (2014).Article
PubMed
Google Scholar
Li, G. et al. Identification of hub genes and potential ceRNA networks of diabetic nephropathy by weighted gene co-expression. Netw. Anal. 12, 1–12 (2021).
Google Scholar
Schaefer, L. Extracellular matrix molecules: Endogenous danger signals as new drug targets in kidney diseases. Curr. Opin. Pharmacol. 10, 185–190 (2010).Article
PubMed
Google Scholar
Zhou, X., Zeng, B., Li, Y., Wang, H. & Zhang, X. Linc02532 contributes to radiosensitivity in clear cell renal cell carcinoma through the mir-654–5p/yy1 axis. Molecules 26, 630 (2021).Article
Google Scholar
Kosasih, F. R. & Bonavida, B. YY1-mediated regulation of type 2 diabetes via insulin. YY1 Control Pathog. Drug Resist. Cancer A Crit. Ther. Target. 623, 271–287 (2020) https://doi.org/10.1016/B978-0-12-821909-6.00005-5.Yang, Y. et al. Identification of prognostic chromatin-remodeling genes in clear cell renal cell carcinoma. Aging (Albany. NY). 12, 25614–25642 (2020).Yao, T., Wang, Q., Zhang, W., Bian, A. & Zhang, J. Identification of genes associated with renal cell carcinoma using gene expression profiling analysis. Oncol. Lett. 12, 73–78 (2016).Article
ADS
PubMed
PubMed Central
Google Scholar
Marzan, A. A., Shahi, S., Arman, M. S., Hasan, M. Z. & Ghosh, A. Probing biological network in concurrent carcinomas and Type-2 diabetes for potential biomarker screening: An advanced computational paradigm. Adv. Biomark. Sci. Technol. 5, 89–104 (2023).
Google Scholar
Peters, I. et al. Decreased mRNA expression of GATA1 and GATA2 is associated with tumor aggressiveness and poor outcome in clear cell renal cell carcinoma. Target. Oncol. 10, 267–275 (2015).Article
PubMed
Google Scholar
Muiya, N. P. et al. A study of the role of GATA2 gene polymorphism in coronary artery disease risk traits. Gene 544, 152–158 (2014).Article
PubMed
Google Scholar
Yeh, I. J. et al. Identification of the potential prognostic markers from the miRNA-lncRNA-mRNA interactions for metastatic renal cancer via next-generation sequencing and bioinformatics. Diagnostics 10, 20–30 (2020).Article
Google Scholar
Zeng, K. et al. Profiling of circulating serum exosomal microRNAs in elderly patients with infectious stress hyperglycaemia. Clin. Transl. Discov. 3, 160 (2023).Article
Google Scholar
Yang, M. et al. MiR-93-5p regulates tumorigenesis and tumor immunity by targeting PD-L1/CCND1 in breast cancer. Ann. Transl. Med. 10, 203–203 (2022).Article
PubMed
PubMed Central
Google Scholar
Zottel, A. et al. Analysis of miR-9-5p, miR-124-3p, miR-21-5p, miR-138-5p, and miR-1-3p in glioblastoma cell lines and extracellular vesicles. Int. J. Mol. Sci. 21, 1–22 (2020).Article
Google Scholar
Zhong, W., Zhang, F., Huang, C., Lin, Y. & Huang, J. Identification of an apoptosis-related prognostic gene signature and molecular subtypes of clear cell renal cell carcinoma (ccRCC). J. Cancer 12, 3265–3276 (2021).Article
PubMed
PubMed Central
Google Scholar
Banumathy, G. & Cairns, P. Signaling pathways in renal cell carcinoma. Cancer Biol. Ther. 10, 658–664 (2010).Article
PubMed
PubMed Central
Google Scholar
Storgaard, H. et al. Insulin signal transduction in skeletal muscle from glucose-intolerant relatives with type 2 diabetes. Diabetes 50, 2770–2778 (2001).Article
PubMed
Google Scholar
Mao, W., Wang, K., Wu, Z., Xu, B. & Chen, M. Current status of research on exosomes in general, and for the diagnosis and treatment of kidney cancer in particular. J. Exp. Clin. Cancer Res. 40, 693 (2021).Article
Google Scholar
Xiao, Y. et al. Extracellular vesicles in type 2 diabetes mellitus: key roles in pathogenesis, complications, and therapy. J. Extracell. Vesicles 8, 968 (2019).Article
Google Scholar
Prajapati, B., Jena, P., Rajput, P., Purandhar, K. & Seshadri, S. Understanding and modulating the toll like receptors (TLRs) and NOD like receptors (NLRs) cross talk in type 2 diabetes. Curr. Diabetes Rev. 10, 190–200 (2014).Article
PubMed
Google Scholar
Jin, J. et al. Novel insights into NOD-like receptors in renal diseases. Acta Pharmacol. Sin. 43, 2789–2806 (2022).Article
PubMed
PubMed Central
Google Scholar
Esteller, M. CpG island hypermethylation and tumor suppressor genes: A booming present, a brighter future. Oncogene 21, 5427–5440 (2002).Article
PubMed
Google Scholar
DNA Methylation and Apoptosis Resistance in Cancer Cells.Lin, T. C. DDX3X is epigenetically repressed in renal cell carcinoma and serves as a prognostic indicator and therapeutic target in cancer progression. Int. J. Mol. Sci. 21, 523 (2020).
Google Scholar
He, Y. L. et al. Evaluation of pharmacokinetic interactions between vildagliptin and digoxin in healthy volunteers. J. Clin. Pharmacol. 47, 998–1004 (2007).Article
PubMed
Google Scholar
Han, M. S. et al. Imatinib mesylate reduces endoplasmic reticulum stress and induces remission of Diabetes in db/db mice. Diabetes 58, 329–336 (2009).Article
PubMed
PubMed Central
Google Scholar
Ryan, C. W. et al. A phase II study of everolimus in combination with imatinib for previously treated advanced renal carcinoma. Invest. New Drugs 29, 374–379 (2011).Article
PubMed
Google Scholar
Sivanand, S. et al. A validated tumorgraft model reveals activity of dovitinib against renal cell carcinoma. Sci. Transl. Med. 4, 96 (2012).Article
Google Scholar
Guven, D. C. et al. The association between albumin levels and survival in patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis. Front. Mol. Biosci. 9, 214586 (2022).Article
Google Scholar
Dall’Olio, F. G. et al. Immortal time bias in the association between toxicity and response for immune checkpoint inhibitors: A meta-analysis. Immunotherapy 13, 257–270 (2021).