• Burden of disease scenarios. For 204 countries and territories, 2022–2050: a forecasting analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2204–56.


    Google Scholar     

  • Sonkin D, Thomas A, Teicher BA. Cancer treatments: past, present, and future. Cancer Genet. 2024;286–287:18–24.


    Google Scholar     

  • Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10–45.


    Google Scholar     

  • Qiong L, Shuyao X, Shan X, Qian F, Jiaying T, Yao X, et al. Recent advances in the glycolytic processes linked to tumor metastasis. Curr Mol Pharmacol. 2024;17:e18761429308361.


    Google Scholar     

  • Joyson P, Karanvir S, Sumit P, Rohit P, Shah Alam K, Bhupinder K, et al. An update on recently developed analytical and Bio-analytical methods for some anticancer drugs. Curr Pharm Anal. 2023;19(2):117–35.


    Google Scholar     

  • Karati D, Kumar D. Molecular insight into the apoptotic mechanism of cancer cells: an explicative review. Curr Mol Pharmacol. 2024;17:e18761429273223.


    Google Scholar     

  • Yuwei Q, Ninghua Y, Fan Z, Shi Q, Xiaolei C, Wenjie Z. Tumor organoid model and its pharmacological applications in tumorigenesis prevention. Curr Mol Pharmacol. 2023;16(4):435–47.


    Google Scholar     

  • Chen F, Wendl MC, Wyczalkowski MA, Bailey MH, Li Y, Ding L. Moving pan-cancer studies from basic research toward the clinic. Nat Cancer. 2021;2(9):879–90.


    Google Scholar     

  • Wang T, Yao S, Li S, Fei X, Zhang M. A prognostic model based on the augmin family genes for LGG patients. Sci Rep. 2023;13(1):7520.


    Google Scholar     

  • Tang L, Chen Z, Wei C, Liu H, Wang B, Yu T, et al. The significance of HAUS1 and its relationship with immune microenvironment in hepatocellular carcinoma. J Cancer. 2024;15(5):1328–41.


    Google Scholar     

  • Zhang X, Zhuang R, Ye Q, Zhuo J, Chen K, Lu D, et al. High expression of human augmincomplex submit 3 indicates poor prognosis and associates with tumor progression in hepatocellular carcinoma. J Cancer. 2019;10(6):1434–43.


    Google Scholar     

  • Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. PLoS Genet. 2020;16(12):e1009218.


    Google Scholar     

  • Zhu H, Fang K, Fang G. FAM29A, a target of Plk1 regulation, controls the partitioning of NEDD1 between the mitotic spindle and the centrosomes. J Cell Sci. 2009;122(Pt 15):2750–9.


    Google Scholar     

  • Rajičić M, Makunin A, Adnađević T, Trifonov V, Vujošević M, Blagojević J. B chromosomes’ sequences in yellow-necked mice Apodemus flavicollis-exploring the transcription. Life. 2021. https://doi.org/10.3390/life12010050.


    Google Scholar     

  • MotieGhader H, Masoudi-Sobhanzadeh Y, Ashtiani SH, Masoudi-Nejad A. mRNA and MicroRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms. Genomics. 2020;112(5):3207–17.


    Google Scholar     

  • Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8.


    Google Scholar     

  • Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Volume 347. New York, NY: Science; 2015. p. 1260419. 6220.


    Google Scholar     

  • Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. Ualcan: an update to the integrated cancer data analysis platform, vol. 25. New York, NY: Neoplasia; 2022. p. 18–27.


    Google Scholar     

  • Shen A, Liu L, Huang Y, Shen Z, Wu M, Chen X, et al. Down-Regulating HAUS6 suppresses cell proliferation by activating the p53/p21 pathway in colorectal cancer. Front Cell Dev Biol. 2021;9:772077.


    Google Scholar     

  • Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.


    Google Scholar     

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. Protein Data Bank Nucleic Acids Res. 2000;28(1):235–42.


    Google Scholar     

  • Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J. Methsurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 2018;10(3):277–88.


    Google Scholar     

  • Zhou Y, Zeng P, Li YH, Zhang Z, Cui Q. Sramp: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res. 2016;44(10):e91.


    Google Scholar     

  • Franz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD, et al. Genemania update 2018. Nucleic Acids Res. 2018;46(W1):W60-w4.


    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. 2019;47(W1):W556-60.


    Google Scholar     

  • Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. ClusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Cambridge (Mass)). 2021;2(3):100141.


    Google Scholar     

  • Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.


    Google Scholar     

  • Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023. https://doi.org/10.1093/genetics/iyad031.


    Google Scholar     

  • Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 2025;53(D1):D672-d7.


    Google Scholar     

  • Han Y, Wang Y, Dong X, Sun D, Liu Z, Yue J, et al. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res. 2023;51(D1):D1425-d31.


    Google Scholar     

  • Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.


    Google Scholar     

  • Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2.


    Google Scholar     

  • Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017. https://doi.org/10.1200/PO.17.00073.


    Google Scholar     

  • Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The immune landscape of cancer. Immunity. 2018;48(4):812–e3014.


    Google Scholar     

  • Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P, et al. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020;12(1):21.


    Google Scholar     

  • Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550–8.


    Google Scholar     

  • Zeng Z, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W, et al. TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Res. 2022;50(D1):D1391-d7.


    Google Scholar     

  • Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res. 2023;51(D1):D1257–62.


    Google Scholar     

  • Liu CJ, Hu FF, Xie GY, Miao YR, Li XW, Zeng Y, et al. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform. 2023. https://doi.org/10.1093/bib/bbac558.


    Google Scholar     

  • Xie H, Yang K, Qin C, Zhou X, Liu J, Nong J, et al. Sarcosine dehydrogenase as an immune infiltration-associated biomarker for the prognosis of hepatocellular carcinoma. J Cancer. 2024;15(1):149–65.


    Google Scholar     

  • Zongtao R, Xiaoyu Z, Jingya H. Expression and prognostic significance of Ferroptosis-related proteins SLC7A11 and GPX4 in renal cell carcinoma. Protein Pept Lett. 2023;30(10):868–76.


    Google Scholar     

  • Qin C, Qin H, Xie H, Li Y, Bi A, Liao X, et al. The role of MATN3 in cancer prognosis and immune infiltration across multiple tumor types. J Cancer. 2025;16(5):1519–37.


    Google Scholar     

  • Lei Z, Huan Y, Jing L, Ke W, Xiang C, Wei X, et al. Metabolomics-based approach to analyze the therapeutic targets and metabolites of a synovitis ointment for knee osteoarthritis. Curr Pharm Anal. 2023;19(3):222–34.


    Google Scholar     

  • Zhou X, Li TM, Luo JZ, Lan CL, Wei ZL, Fu TH, et al. CYP2C8 suppress Proliferation, Migration, invasion and Sorafenib resistance of hepatocellular carcinoma via PI3K/Akt/p27(kip1) axis. J Hepatocell Carcinoma. 2021;8:1323–38.


    Google Scholar     

  • Oerum S, Meynier V, Catala M, Tisné C. A comprehensive review of m6A/m6Am RNA methyltransferase structures. Nucleic Acids Res. 2021;49(13):7239–55.


    Google Scholar     

  • Lee AV, Nestler KA, Chiappinelli KB. Therapeutic targeting of DNA methylation alterations in cancer. Pharmacol Ther. 2024;258:108640.


    Google Scholar     

  • Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94.


    Google Scholar     

  • Cordani M, Dando I, Ambrosini G, González-Menéndez P. Signaling, cancer cell plasticity, and intratumor heterogeneity. Cell Commun Signal. 2024;22(1):255.


    Google Scholar     

  • Loh JJ, Ma S. Hallmarks of cancer stemness. Cell Stem Cell. 2024;31(5):617–39.


    Google Scholar     

  • Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell. 2023;41(3):404–20.


    Google Scholar     

  • Gao X, Liu D-D, Liu J-Z, Wang R. TCGA-based analysis of oncogenic signaling pathways underlying oral squamous cell carcinoma. Oncol Transl Med. 2024;10(2):87–92.


    Google Scholar     

  • Wang Y, Wang J, Zeng T, Qi J. Data-mining-based biomarker evaluation and experimental validation of SHTN1 for bladder cancer. Cancer Genet. 2024;288–289:43–53.


    Google Scholar     

  • Liu H, Guo Z, Wang P. Genetic expression in cancer research: challenges and complexity. Gene Rep. 2024;37:102042.


    Google Scholar     

  • Liu H, Li Y, Karsidag M, Tu T, Wang P. Technical and biological biases in bulk transcriptomic data mining for cancer research. J Cancer. 2025;16(1):34–43.


    Google Scholar     

  • Yao Z, Chen J, Wang Y, Cao L. Bioinformatics analysis and validation of HAUS6 as a key prognostic gene in squamous cell carcinoma of the tongue. Arch Oral Biol. 2024;164:106000.


    Google Scholar     

  • Kim H, Nguyen NP, Turner K, Wu S, Gujar AD, Luebeck J, et al. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat Genet. 2020;52(9):891–7.


    Google Scholar     

  • Shoshani O, Brunner SF, Yaeger R, Ly P, Nechemia-Arbely Y, Kim DH, et al. Chromothripsis drives the evolution of gene amplification in cancer. Nature. 2021;591(7848):137–41.


    Google Scholar     

  • Lee JS. The mutational landscape of hepatocellular carcinoma. Clin Mol Hepatol. 2015;21(3):220–9.


    Google Scholar     

  • Yu J, Ling S, Hong J, Zhang L, Zhou W, Yin L, et al. TP53/mTORC1-mediated bidirectional regulation of PD-L1 modulates immune evasion in hepatocellular carcinoma. J Immunother Cancer. 2023. https://doi.org/10.1136/jitc-2023-007479.


    Google Scholar     

  • Nishiyama A, Nakanishi M. Navigating the DNA methylation landscape of cancer. Trends Genet. 2021;37(11):1012–27.


    Google Scholar     

  • Palmeri M, Mehnert J, Silk AW, Jabbour SK, Ganesan S, Popli P, et al. Real-world application of tumor mutational burden-high (TMB-high) and microsatellite instability (MSI) confirms their utility as immunotherapy biomarkers. ESMO Open. 2022;7(1):100336.


    Google Scholar     

  • Zhu J, Zhang T, Li J, Lin J, Liang W, Huang W, et al. Association between tumor mutation burden (TMB) and outcomes of cancer patients treated with PD-1/PD-L1 inhibitions: a meta-analysis. Front Pharmacol. 2019;10:673.


    Google Scholar     

  • Miranda A, Hamilton PT, Zhang AW, Pattnaik S, Becht E, Mezheyeuski A, et al. Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci U S A. 2019;116(18):9020–9.


    Google Scholar     

  • Zhang Z, Wang ZX, Chen YX, Wu HX, Yin L, Zhao Q, et al. Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response. Genome Med. 2022;14(1):45.


    Google Scholar     

  • Wu H, Geng Q, Shi W, Qiu C. Comprehensive pan-cancer analysis reveals CCDC58 as a carcinogenic factor related to immune infiltration. Apoptosis. 2024;29(3–4):536–55.


    Google Scholar     

  • Wang Y, Xie Y, Qian L, Ding R, Pang R, Chen P, et al. RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance. Front Pharmacol. 2024;15:1445170.


    Google Scholar     

  • Xu X, Xu Y, Hu W, Hong W, Wang Y, Zhang X, et al. Stromal score is a promising index in tumor patients’ outcome determination. Heliyon. 2023;9(11):e22432.


    Google Scholar     

  • Wu Y, Ma J, Yang X, Nan F, Zhang T, Ji S, et al. Neutrophil profiling illuminates anti-tumor antigen-presenting potency. Cell. 2024;187(6):1422–e3924.


    Google Scholar     

  • Cha JH, Chan LC, Li CW, Hsu JL, Hung MC. Mechanisms controlling PD-L1 expression in cancer. Mol Cell. 2019;76(3):359–70.


    Google Scholar     

  • Francisco LM, Sage PT, Sharpe AH. The PD-1 pathway in tolerance and autoimmunity. Immunol Rev. 2010;236:219–42.


    Google Scholar     

  • Lin X, Kang K, Chen P, Zeng Z, Li G, Xiong W, et al. Regulatory mechanisms of PD-1/PD-L1 in cancers. Mol Cancer. 2024;23(1):108.


    Google Scholar     

  • Liu H, Yang Z. Time management and personal efficiency in the age of computational and systems oncology. Comput Syst Oncol. 2024;4(1):e70001.


    Google Scholar