MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

References

• Addala, V., Newell, F., Pearson, J. V., Redwood, A., Robinson, B. W., Creaney, J., & Waddell, N. (2024). Computational immunogenomic approaches to predict response to cancer immunotherapies. Nature Reviews. Clinical Oncology, 21(1), 28–46. https://doi.org/10.1038/s41571-023-00830-6
   PubMed Web of Science ®Google Scholar
• Anbalagan, T., Nath, M. K., Vijayalakshmi, D., & Anbalagan, A. (2023). Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Engineering Advances, 6, 100089. https://doi.org/10.1016/j.bea.2023.100089
   Google Scholar
• Aono, Y., Horinaka, M., Iizumi, Y., Watanabe, M., Taniguchi, T., Yasuda, S., & Sakai, T. (2018). Sulindac sulfone inhibits the mTORC1 pathway in colon cancer cells by directly targeting voltage-dependent anion channel 1 and 2. Biochemical and Biophysical Research Communications, 505(4), 1203–1210.
   PubMed Web of Science ®Google Scholar
• Aran, D., Hu, Z., & Butte, A. J. (2017). xCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biology, 18(1), 220. https://doi.org/10.1186/s13059-017-1349-1
   PubMed Web of Science ®Google Scholar
• Bogunovic, D., O'Neill, D. W., Belitskaya-Levy, I., Vacic, V., Yu, Y.-L., Adams, S., Darvishian, F., Berman, R., Shapiro, R., Pavlick, A. C., Lonardi, S., Zavadil, J., Osman, I., & Bhardwaj, N. (2009). Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proceedings of the National Academy of Sciences of the United States of America, 106(48), 20429–20434. https://doi.org/10.1073/pnas.0905139106
   PubMed Web of Science ®Google Scholar
• Carlino, M. S., Larkin, J., & Long, G. V. (2021). Immune checkpoint inhibitors in melanoma. Lancet (London, England), 398(10304), 1002–1014. https://doi.org/10.1016/S0140-6736(21)01206-X
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., Byemerwa, J., Krebs, T., Lim, F., Chang, C.-Y., & McDonnell, D. P. (2023). Estrogen receptor signaling in the immune system. Endocrine Reviews, 44(1), 117–141. https://doi.org/10.1210/endrev/bnac017
   PubMed Web of Science ®Google Scholar
• Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., & Alizadeh, A. A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in Molecular Biology (Clifton, N.J.), 1711, 243–259. https://doi.org/10.1007/978-1-4939-7493-1_12
   PubMedGoogle Scholar
• Cirenajwis, H., Ekedahl, H., Lauss, M., Harbst, K., Carneiro, A., Enoksson, J., Rosengren, F., Werner-Hartman, L., Törngren, T., Kvist, A., Fredlund, E., Bendahl, P.-O., Jirström, K., Lundgren, L., Howlin, J., Borg, Å., Gruvberger-Saal, S. K., Saal, L. H., Nielsen, K., … Jönsson, G. (2015). Molecular stratification of metastatic melanoma using gene expression profiling: Prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget, 6(14), 12297–12309. https://doi.org/10.18632/oncotarget.3655
   PubMed Web of Science ®Google Scholar
• Cruz-Rodriguez, N., Combita, A. L., Enciso, L. J., Quijano, S. M., Pinzon, P. L., Lozano, O. C., Castillo, J. S., Li, L., Bareño, J., Cardozo, C., Solano, J., Herrera, M. V., Cudris, J., & Zabaleta, J. (2016). High expression of ID family and IGJ genes signature as predictor of low induction treatment response and worst survival in adult Hispanic patients with B-acute lymphoblastic leukemia. Journal of Experimental & Clinical Cancer Research: CR, 35(1), 64. https://doi.org/10.1186/s13046-016-0333-z
   PubMedGoogle Scholar
• Di Fazio, A., & Gullerova, M. (2023). An old friend with a new face: TRNA-derived small RNAs with big regulatory potential in cancer biology. British Journal of Cancer, 128(9), 1625–1635. https://doi.org/10.1038/s41416-023-02191-4
   PubMed Web of Science ®Google Scholar
• Di Raimondo, C., Lozzi, F., Di Domenico, P. P., Campione, E., & Bianchi, L. (2023). The Diagnosis and Management of Cutaneous Metastases from Melanoma. International Journal of Molecular Sciences, 24(19), 14535. https://doi.org/10.3390/ijms241914535
   PubMed Web of Science ®Google Scholar
• Di, S., Fan, C., Ma, Z., Li, M., Guo, K., Han, D., Li, X., Mu, D., & Yan, X. (2019). PERK/eIF-2α/CHOP pathway dependent ROS generation mediates butein-induced non-small-cell lung cancer apoptosis and G2/M phase arrest. International Journal of Biological Sciences, 15(8), 1637–1653. https://doi.org/10.7150/ijbs.33790
   PubMed Web of Science ®Google Scholar
• Dong, K., Gu, D., Shi, J., Bao, Y., Fu, Z., Fang, Y., Qu, L., Zhu, W., Jiang, A., & Wang, L. (2022). Identification and verification of m7G modification patterns and characterization of tumor microenvironment infiltration via multi-omics analysis in clear cell renal cell carcinoma. Frontiers in Immunology, 13, 874792. https://doi.org/10.3389/fimmu.2022.874792
   PubMedGoogle Scholar
• Elangovan, P., & Nath, M. K. (2021). A novel shallow ConvNet-18 for malaria parasite detection in thin blood smear images: CNN based malaria parasite detection. SN Computer Science, 2(5), 380. https://doi.org/10.1007/s42979-021-00763-w
   Google Scholar
• Elangovan, P., & Nath, M. K. (2022). En‐ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks. International Journal of Imaging Systems and Technology, 32(6), 2034–2048. https://doi.org/10.1002/ima.22761
   Web of Science ®Google Scholar
• Elangovan, P., Vijayalakshmi, D., & Nath, M. K. (2024). COVID-19Net: An effective and robust approach for COVID-19 detection using ensemble of ConvNet-24 and customized pre-trained models. Circuits, Systems, and Signal Processing, 43(4), 2385–2408. https://doi.org/10.1007/s00034-023-02564-3
   Web of Science ®Google Scholar
• Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., Krogsdam, A., Loncova, Z., Posch, W., Wilflingseder, D., Sopper, S., Ijsselsteijn, M., Brouwer, T. P., Johnson, D., Xu, Y., Wang, Y., Sanders, M. E., Estrada, M. V., Ericsson-Gonzalez, P., . . . Trajanoski, Z. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Medicine, 11(1), 34. https://doi.org/10.1186/s13073-019-0638-6
   Google Scholar
• Guo, W., Wang, H., & Li, C. (2021). Signal pathways of melanoma and targeted therapy. Signal Transduction and Targeted Therapy, 6(1), 424. https://doi.org/10.1038/s41392-021-00827-6
   PubMed Web of Science ®Google Scholar
• Hänzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14(1), 7. https://doi.org/10.1186/1471-2105-14-7
   PubMed Web of Science ®Google Scholar
• Harbst, K., Staaf, J., Lauss, M., Karlsson, A., Måsbäck, A., Johansson, I., Bendahl, P.-O., Vallon-Christersson, J., Törngren, T., Ekedahl, H., Geisler, J., Höglund, M., Ringnér, M., Lundgren, L., Jirström, K., Olsson, H., Ingvar, C., Borg, Å., Tsao, H., & Jönsson, G. (2012). Molecular profiling reveals low- and high-grade forms of primary melanoma. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 18(15), 4026–4036. https://doi.org/10.1158/1078-0432.CCR-12-0343
   PubMed Web of Science ®Google Scholar
• He, Y., & Wang, X. (2023). Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis. Computers in Biology and Medicine, 167, 107591. https://doi.org/10.1016/j.compbiomed.2023.107591
   PubMed Web of Science ®Google Scholar
• Jiang, A., Bao, Y., Wang, A., Gong, W., Gan, X., Wang, J., Bao, Y., Wu, Z., Liu, B., Lu, J., & Wang, L. (2022). Establishment of a prognostic prediction and drug selection model for patients with clear cell renal cell carcinoma by multiomics data analysis. Oxidative Medicine and Cellular Longevity, 2022, e3617775–30. https://doi.org/10.1155/2022/3617775
   PubMed Web of Science ®Google Scholar
• Jiang, A., Luo, P., Chen, M., Fang, Y., Liu, B., Wu, Z., Qu, L., Wang, A., Wang, L., & Cai, C. (2022). A new thinking: Deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma. Cell & Bioscience, 12(1), 209. https://doi.org/10.1186/s13578-022-00948-7
   PubMed Web of Science ®Google Scholar
• Jiang, A., Meng, J., Bao, Y., Wang, A., Gong, W., Gan, X., Wang, J., Bao, Y., Wu, Z., Lu, J., Liu, B., & Wang, L. (2021). Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal cell carcinoma. Frontiers in Oncology, 11, 755212. https://doi.org/10.3389/fonc.2021.755212
   PubMed Web of Science ®Google Scholar
• Jiang, A., Meng, J., Gong, W., Zhang, Z., Gan, X., Wang, J., Wu, Z., Liu, B., Qu, L., & Wang, L. (2022). Elevated SNRPA1, as a promising predictor reflecting severe clinical outcome via effecting tumor immunity for ccRCC, is related to cell invasion, metastasis, and sunitinib sensitivity. Frontiers in Immunology, 13, 842069. https://doi.org/10.3389/fimmu.2022.842069
   PubMed Web of Science ®Google Scholar
• Jiang, A., Wu, X., Wang, D., Wang, A., Dong, K., Liu, B., Qu, L., Luo, P., Wang, J., Tong, Q., & Wang, L. (2022). A new thinking: Deciphering the aberrance and clinical implication of IGF axis regulation pattern in clear cell renal cell carcinoma. Frontiers in Immunology, 13, 935595. https://doi.org/10.3389/fimmu.2022.935595
   PubMed Web of Science ®Google Scholar
• Jiang, A., Xu, Z., Fang, X., Gu, D., Dong, K., Wu, Z., Gan, X., Liu, B., Ge, S., Qu, L., Luo, P., Wang, A., & Wang, L. (2023). RNA modification pattern-based subtypes reveal heterogenous clinical outcomes and tumor immunity of clear cell renal cell carcinoma. MedComm – Future Medicine, 2(1), e30. https://doi.org/10.1002/mef2.30
   Google Scholar
• Kim, H., Yu, S. J., Yeo, I., Cho, Y. Y., Lee, D. H., Cho, Y., Cho, E. J., Lee, J.-H., Kim, Y. J., Lee, S., Jun, J., Park, T., Yoon, J.-H., & Kim, Y. (2017). Prediction of response to sorafenib in hepatocellular carcinoma: A putative marker panel by multiple reaction monitoring-mass spectrometry (MRM-MS). Molecular & Cellular Proteomics: MCP, 16(7), 1312–1323. https://doi.org/10.1074/mcp.M116.066704
   PubMed Web of Science ®Google Scholar
• Knackstedt, T., Knackstedt, R. W., Couto, R., & Gastman, B. (2018). Malignant melanoma: Diagnostic and management update. Plastic and Reconstructive Surgery, 142(2), 202e–216e. https://doi.org/10.1097/PRS.0000000000004571
   PubMed Web of Science ®Google Scholar
• Kohsaka, S., Tada, Y., Ando, M., Nakaguro, M., Shirai, Y., Ueno, T., Kojima, S., Hirai, H., Saigusa, N., Kano, S., Tsukahara, K., Togashi, T., Ozawa, H., Kondo, T., Okami, K., Takahashi, H., Kawakita, D., Fushimi, C., Suzuki, T., … Mano, H. (2022). Identification of novel prognostic and predictive biomarkers in salivary duct carcinoma via comprehensive molecular profiling. NPJ Precision Oncology, 6(1), 82. https://doi.org/10.1038/s41698-022-00324-1
   PubMed Web of Science ®Google Scholar
• Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., Li, B., & Liu, X. S. (2017). TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Research, 77(21), e108–e110. https://doi.org/10.1158/0008-5472.CAN-17-0307
   PubMed Web of Science ®Google Scholar
• Li, Y., Yu, P., Long, J., Tang, L., Zhang, X., Zhou, Z., Cao, D., Su, J., Chen, X., & Peng, C. (2022). A novel ribosomal protein S6 kinase 2 inhibitor attenuates the malignant phenotype of cutaneous malignant melanoma cells by inducing cell cycle arrest and apoptosis. Bioengineered, 13(5), 13555–13570. https://doi.org/10.1080/21655979.2022.2080364
   PubMed Web of Science ®Google Scholar
• Liang, X., Hu, K., Li, D., Wang, Y., Liu, M., Wang, X., Zhu, W., Wang, X., Yang, Z., & Lu, J. (2020). Identification of core genes and potential drugs for castration-resistant prostate cancer based on bioinformatics analysis. DNA and Cell Biology, 39(5), 836–847. https://doi.org/10.1089/dna.2019.5247
   PubMed Web of Science ®Google Scholar
• Liu, Z., Liu, L., Weng, S., Guo, C., Dang, Q., Xu, H., Wang, L., Lu, T., Zhang, Y., Sun, Z., & Han, X. (2022). Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nature Communications, 13(1), 816. https://doi.org/10.1038/s41467-022-28421-6
   PubMed Web of Science ®Google Scholar
• Liu, Z., Xu, H., Weng, S., Guo, C., Dang, Q., Zhang, Y., Ren, Y., Liu, L., Wang, L., Ge, X., Xing, Z., Zhang, J., Luo, P., & Han, X. (2023). Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management. Cancer Immunology, Immunotherapy: CII, 72(3), 599–615. https://doi.org/10.1007/s00262-022-03279-1
   PubMed Web of Science ®Google Scholar
• Long, G. V., Swetter, S. M., Menzies, A. M., Gershenwald, J. E., & Scolyer, R. A. (2023). Cutaneous melanoma. Lancet (London, England), 402(10400), 485–502. https://doi.org/10.1016/S0140-6736(23)00821-8
   PubMed Web of Science ®Google Scholar
• Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8
   PubMed Web of Science ®Google Scholar
• Lu, X., Meng, J., Su, L., Jiang, L., Wang, H., Zhu, J., Huang, M., Cheng, W., Xu, L., Ruan, X., Yeh, S., Liang, C., & Yan, F. (2021a). Multi-omics consensus ensemble refines the classification of muscle-invasive bladder cancer with stratified prognosis, tumour microenvironment and distinct sensitivity to frontline therapies. Clinical and Translational Medicine, 11(12), e601. https://doi.org/10.1002/ctm2.601
   PubMed Web of Science ®Google Scholar
• Lu, X., Meng, J., Zhou, Y., Jiang, L., & Yan, F. (2021b). MOVICS: An R package for multi-omics integration and visualization in cancer subtyping. Bioinformatics, 36(22–23), 5539–5541. https://doi.org/10.1093/bioinformatics/btaa1018
   Web of Science ®Google Scholar
• Mayakonda, A., Lin, D. C., Assenov, Y., Plass, C., & Koeffler, H. P. (2018). Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Research, 28(11), 1747–1756. https://doi.org/10.1101/gr.239244.118
   PubMed Web of Science ®Google Scholar
• Meng, J., Jiang, A., Lu, X., Gu, D., Ge, Q., Bai, S., Zhou, Y., Zhou, J., Hao, Z., Yan, F., Wang, L., Wang, H., Du, J., & Liang, C. (2023). Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy. iMeta, 2(4), e147. https://doi.org/10.1002/imt2.147
   Google Scholar
• Meraz-Torres, F., Niessner, H., Plöger, S., Riel, S., Schörg, B., Casadei, N., Kneilling, M., Schaller, M., Flatz, L., Macek, B., Eigentler, T., Rieß, O., Garbe, C., Amaral, T., & Sinnberg, T. (2024). Augmenting MEK inhibitor efficacy in BRAF wild-type melanoma: Synergistic effects of disulfiram combination therapy. Journal of Experimental & Clinical Cancer Research: CR, 43(1), 30. https://doi.org/10.1186/s13046-023-02941-5
   PubMed Web of Science ®Google Scholar
• Newell, F., Pires da Silva, I., Johansson, P. A., Menzies, A. M., Wilmott, J. S., Addala, V., Carlino, M. S., Rizos, H., Nones, K., Edwards, J. J., Lakis, V., Kazakoff, S. H., Mukhopadhyay, P., Ferguson, P. M., Leonard, C., Koufariotis, L. T., Wood, S., Blank, C. U., Thompson, J. F., … Long, G. V. (2022). Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance. Cancer Cell, 40(1), 88–102.e7. https://doi.org/10.1016/j.ccell.2021.11.012
   PubMed Web of Science ®Google Scholar
• Noviello, T. M. R., Di Giacomo, A. M., Caruso, F. P., Covre, A., Mortarini, R., Scala, G., Costa, M. C., Coral, S., Fridman, W. H., Sautès-Fridman, C., Brich, S., Pruneri, G., Simonetti, E., Lofiego, M. F., Tufano, R., Bedognetti, D., Anichini, A., Maio, M., & Ceccarelli, M. (2023). Guadecitabine plus ipilimumab in unresectable melanoma: Five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial. Nature Communications, 14(1), 5914. https://doi.org/10.1038/s41467-023-40994-4
   PubMedGoogle Scholar
• Nsengimana, J., Laye, J., Filia, A., O'Shea, S., Muralidhar, S., Poźniak, J., Droop, A., Chan, M., Walker, C., Parkinson, L., Gascoyne, J., Mell, T., Polso, M., Jewell, R., Randerson-Moor, J., Cook, G. P., Bishop, D. T., & Newton-Bishop, J. (2018). β-Catenin-mediated immune evasion pathway frequently operates in primary cutaneous melanomas. The Journal of Clinical Investigation, 128(5), 2048–2063. https://doi.org/10.1172/JCI95351
   PubMed Web of Science ®Google Scholar
• Pérez-Fernández, A., López-Ruano, G., Prieto-Bermejo, R., Ijurko, C., Díez-Campelo, M., Sánchez-Guijo, F., & Hernández-Hernández, Á. (2019). SHP1 and SHP2 inhibition enhances the pro-differentiative effect of phorbol esters: An alternative approach against acute myeloid leukemia. Journal of Experimental & Clinical Cancer Research: CR, 38(1), 80. https://doi.org/10.1186/s13046-019-1097-z
   PubMedGoogle Scholar
• Pontecorvi, G., Bellenghi, M., Tait, S., Tirelli, V., Matarrese, P., Mattia, G., Carè, A., & Puglisi, R. (2022). Different susceptibilities of human melanoma cell lines to G2/M blockage and cell death activation in response to the estrogen receptor β agonist LY500307. Journal of Cancer, 13(5), 1573–1587. https://doi.org/10.7150/jca.65425
   PubMed Web of Science ®Google Scholar
• Poźniak, J., Nsengimana, J., Laye, J. P., O'Shea, S. J., Diaz, J. M. S., Droop, A. P., Filia, A., Harland, M., Davies, J. R., Mell, T., Randerson-Moor, J. A., Muralidhar, S., Hogan, S. A., Freiberger, S. N., Levesque, M. P., Cook, G. P., Bishop, D. T., & Newton-Bishop, J. (2019). Genetic and environmental determinants of immune response to cutaneous melanoma. Cancer Research, 79(10), 2684–2696. https://doi.org/10.1158/0008-5472.CAN-18-2864
   PubMed Web of Science ®Google Scholar
• Racle, J., & Gfeller, D. (2020). EPIC: A tool to estimate the proportions of different cell types from bulk gene expression data. Methods in Molecular Biology (Clifton, N.J.), 2120, 233–248.
   PubMedGoogle Scholar
• Radke, J., Schumann, E., Onken, J., Koll, R., Acker, G., Bodnar, B., Senger, C., Tierling, S., Möbs, M., Vajkoczy, P., Vidal, A., Högler, S., Kodajova, P., Westphal, D., Meier, F., Heppner, F., Kreuzer-Redmer, S., Grebien, F., Jürchott, K., & Redmer, T. (2022). Decoding molecular programs in melanoma brain metastases. Nature Communications, 13(1), 7304. https://doi.org/10.1038/s41467-022-34899-x
   PubMed Web of Science ®Google Scholar
• Robert, C., & Gautheret, D. (2022). Multi-omics prediction in melanoma immunotherapy: A new brick in the wall. Cancer Cell, 40(1), 14–16. https://doi.org/10.1016/j.ccell.2021.12.008
   PubMed Web of Science ®Google Scholar
• Roshni Thanka, M., Bijolin Edwin, E., Ebenezer, V., Martin Sagayam, K., Jayakeshav Reddy, B., Günerhan, H., & Emadifar, H. (2023). A hybrid approach for melanoma classification using ensemble machine learning techniques with deep transfer learning. Computer Methods and Programs in Biomedicine Update, 3, 100103. https://doi.org/10.1016/j.cmpbup.2023.100103
   Google Scholar
• Roth, S. C. (2019). What is genomic medicine? Journal of the Medical Library Association: JMLA, 107(3), 442–448. https://doi.org/10.5195/jmla.2019.604
   PubMed Web of Science ®Google Scholar
• Shi, C., Qin, K., Lin, A., Jiang, A., Cheng, Q., Liu, Z., Zhang, J., & Luo, P. (2022). The role of DNA damage repair (DDR) system in response to immune checkpoint inhibitor (ICI) therapy. Journal of Experimental & Clinical Cancer Research: CR, 41(1), 268. https://doi.org/10.1186/s13046-022-02469-0
   PubMed Web of Science ®Google Scholar
• Siegel, R. L., Giaquinto, A. N., & Jemal, A. (2024). Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74(1), 12–49. https://doi.org/10.3322/caac.21820
   PubMed Web of Science ®Google Scholar
• Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1), 7–33. https://doi.org/10.3322/caac.21708
   PubMed Web of Science ®Google Scholar
• Villani, A., Potestio, L., Fabbrocini, G., Troncone, G., Malapelle, U., & Scalvenzi, M. (2022). The treatment of advanced melanoma: Therapeutic update. International Journal of Molecular Sciences, 23(12), 6388. https://doi.org/10.3390/ijms23126388
   PubMed Web of Science ®Google Scholar
• Wan, B., Liu, B., Huang, Y., & Lv, C. (2020). Identification of genes of prognostic value in the ccRCC microenvironment from TCGA database. Molecular Genetics & Genomic Medicine, 8(4), e1159. https://doi.org/10.1002/mgg3.1159
   PubMed Web of Science ®Google Scholar
• Wang, K., Wang, X., Zhang, M., Ying, Z., Zhu, Z., Tam, K. Y., Li, C., Zhou, G., Gao, F., Zeng, M., Sze, S. C. W., Wang, X., & Sha, O. (2023). Trichosanthin promotes anti-tumor immunity through mediating chemokines and granzyme B secretion in hepatocellular carcinoma. International Journal of Molecular Sciences, 24(2), 1416. https://doi.org/10.3390/ijms24021416
   PubMed Web of Science ®Google Scholar
• Wei, X., Gu, X., Ma, M., & Lou, C. (2019). Long noncoding RNA HCP5 suppresses skin cutaneous melanoma development by regulating RARRES3 gene expression via sponging miR-12. OncoTargets and Therapy, 12, 6323–6335. https://doi.org/10.2147/OTT.S195796
   PubMed Web of Science ®Google Scholar
• Xu, L., Deng, C., Pang, B., Zhang, X., Liu, W., Liao, G., Yuan, H., Cheng, P., Li, F., Long, Z., Yan, M., Zhao, T., Xiao, Y., & Li, X. (2018). TIP: A web server for resolving tumor immunophenotype profiling. Cancer Research, 78(23), 6575–6580. https://doi.org/10.1158/0008-5472.CAN-18-0689
   PubMed Web of Science ®Google Scholar
• Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. Omics: A Journal of Integrative Biology, 16(5), 284–287. https://doi.org/10.1089/omi.2011.0118
   PubMed Web of Science ®Google Scholar
• Zeng, D., Ye, Z., Shen, R., Yu, G., Wu, J., Xiong, Y., Zhou, R., Qiu, W., Huang, N., Sun, L., Li, X., Bin, J., Liao, Y., Shi, M., & Liao, W. (2021). IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Frontiers in Immunology, 12, 687975. https://doi.org/10.3389/fimmu.2021.687975
   PubMed Web of Science ®Google Scholar
• Zhang, S., Xiong, Y., Zheng, C., Long, J., Zhou, H., Zeng, Z., Ouyang, Y., & Tang, F. (2022). Crosstalk between four types of RNA modification writers characterizes the tumor immune microenvironment infiltration patterns in skin cutaneous melanoma. Frontiers in Cell and Developmental Biology, 10, 821678. https://doi.org/10.3389/fcell.2022.821678
   PubMed Web of Science ®Google Scholar
• Zhang, X., Miao, Y., Sun, H.-W., Wang, Y.-X., Zhao, W.-M., Pang, A.-Y., Wu, X.-Y., Shen, C.-C., & Chen, X.-D. (2022). Integrated analysis from multi-center studies identities m7G-derived modification pattern and risk stratification system in skin cutaneous melanoma. Frontiers in Immunology, 13, 1034516. https://doi.org/10.3389/fimmu.2022.1034516
   PubMed Web of Science ®Google Scholar
• Zhao, L., Zhang, W., Luan, F., Chen, X., Wu, H., He, Q., Weng, Q., Ding, L., & Yang, B. (2023). Butein suppresses PD-L1 expression via downregulating STAT1 in non-small cell lung cancer. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie, 157, 114030. https://doi.org/10.1016/j.biopha.2022.114030
   PubMed Web of Science ®Google Scholar