{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:02:53Z","timestamp":1785420173902,"version":"3.56.0"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2023YFB4503000"],"award-info":[{"award-number":["2023YFB4503000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473129"],"award-info":[{"award-number":["62473129"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62473129"],"award-info":[{"award-number":["62473129"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of Tianjin","doi-asserted-by":"publisher","award":["23JCYBJC00790"],"award-info":[{"award-number":["23JCYBJC00790"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background\/Objectives: Integrating multi-omics data for cancer prognosis remains a challenging problem in bioinformatics because molecular profiles are high-dimensional, heterogeneous, and structured by incomplete biological relationships. Pathway databases provide biologically meaningful prior knowledge for modeling gene-level associations, but the sparsity and local incompleteness of pathway-derived networks often limit the performance of graph-based survival models. This study aimed to develop a pathway-guided framework for improving multi-omics survival prediction and identifying biologically relevant prognostic signals. Methods: We proposed PANA-Surv, a pathway-guided adaptive neighborhood augmentation framework for multi-omics cancer survival analysis. In this framework, KEGG pathways were used to construct gene graphs, and gene-level multi-omics profiles were encoded as node features. A conditional variational autoencoder module (PANA-VAE) was designed to enhance local representations through neighborhood reconstruction and adaptive weighting. The augmented features were then integrated into a graph convolutional survival model optimized with the Cox partial likelihood. Results: PANA-Surv was evaluated on 10 cancer cohorts from The Cancer Genome Atlas (TCGA). The proposed method achieved the highest mean concordance index (C-index) among all compared models and significantly outperformed Cox-EN, DeepSurv, GraphSurv, and LAGProg (all p &lt; 0.01). Ablation analyses showed that both neighborhood reconstruction and adaptive weighting contributed to the observed performance gains, and KEGG-guided graph construction was more effective than alternative graph construction strategies. In a breast cancer (BRCA) case study, PANA-Surv identified 18 prognostic genes, including 12 genes supported by previous studies and 6 potentially novel candidates. Conclusions: These findings indicate that the integration of pathway prior knowledge with adaptive local feature enhancement can improve multi-omics survival modeling and support the identification of biologically relevant prognostic signals associated with cancer outcomes.<\/jats:p>","DOI":"10.3390\/genes17060597","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T12:14:52Z","timestamp":1779452092000},"page":"597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PANA-Surv: A Pathway-Guided Adaptive Neighborhood Augmentation Framework Using KEGG Pathways for Multi-Omics Cancer Prognosis"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiaowen","family":"Cao","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China"},{"name":"Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Science, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4728-2343","authenticated-orcid":false,"given":"Xuekui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-peng","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Zhuo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"He","sequence":"additional","affiliation":[{"name":"School of Science, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhua","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143838","DOI":"10.1016\/j.ijbiomac.2025.143838","article-title":"The role of biological macromolecules in the regulation of angiogenesis in glioblastoma: Focus on vascular growth factors, integrins, and extracellular matrix proteins","volume":"311","author":"Zabihi","year":"2025","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s00018-009-0180-6","article-title":"RNA-seq: From technology to biology","volume":"67","author":"Marguerat","year":"2010","journal-title":"Cell. Mol. Life Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7414","DOI":"10.1073\/pnas.1703577114","article-title":"DNA methylation markers for diagnosis and prognosis of common cancers","volume":"114","author":"Hao","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"433","DOI":"10.14348\/molcells.2021.0042","article-title":"Integrative multi-omics approaches in cancer research: From biological networks to clinical subtypes","volume":"44","author":"Heo","year":"2021","journal-title":"Mol. Cells"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41416-024-02706-7","article-title":"Graph machine learning for integrated multi-omics analysis","volume":"131","author":"Valous","year":"2024","journal-title":"Br. J. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1186\/s12967-024-05976-0","article-title":"Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer","volume":"22","author":"Ren","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"btad472","DOI":"10.1093\/bioinformatics\/btad472","article-title":"FGCNSurv: Dually fused graph convolutional network for multi-omics survival prediction","volume":"39","author":"Wen","year":"2023","journal-title":"Bioinformatics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lin, Y., Golovnina, K., Chen, Z.-X., Lee, H.N., Negron, Y.L.S., Sultana, H., Oliver, B., and Harbison, S.T. (2016). Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster. BMC Genom., 17.","DOI":"10.1186\/s12864-015-2353-z"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e949","DOI":"10.14806\/ej.26.0.949","article-title":"KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways","volume":"26","author":"Chanumolu","year":"2021","journal-title":"EMBnet J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"D277","DOI":"10.1093\/nar\/gkh063","article-title":"The KEGG resource for deciphering the genome","volume":"32","author":"Kanehisa","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymeth.2023.02.011","article-title":"Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis","volume":"213","author":"Zhang","year":"2023","journal-title":"Methods"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: A comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"vbae036","DOI":"10.1093\/bioadv\/vbae036","article-title":"Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning","volume":"4","author":"Cappelletti","year":"2024","journal-title":"Bioinform. Adv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/3575637.3575646","article-title":"Data augmentation for deep graph learning: A survey","volume":"24","author":"Ding","year":"2022","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100527","DOI":"10.1016\/j.cosrev.2022.100527","article-title":"Towards data augmentation in graph neural network: An overview and evaluation","volume":"47","author":"Adjeisah","year":"2023","journal-title":"Comput. Sci. Rev."},{"key":"ref_18","unstructured":"Liu, S., Ying, R., Dong, H., Li, L., Xu, T., Rong, Y., Zhao, P., Huang, J., and Wu, D. (2022, January 17\u201323). Local Augmentation for Graph Neural Networks. Proceedings of the 39th International Conference on Machine Learning, PMLR, Baltimore, MD, USA."},{"key":"ref_19","unstructured":"Huang, X., Ma, Z., Meng, D., Liu, Y., Ruan, S., Sun, Q., Zheng, X., and Qiao, Z. (March, January 25). PRAGA: Prototype-Aware Graph Adaptive Aggregation for Spatial Multi-Modal Omics Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dai, E., Jin, W., Liu, H., and Wang, S. (2022, January 21\u201325). Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event, AZ, USA.","DOI":"10.1145\/3488560.3498408"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100758","DOI":"10.1016\/j.patter.2023.100758","article-title":"Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease","volume":"4","author":"Burkhart","year":"2023","journal-title":"Patterns"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v039.i05","article-title":"Regularization paths for Cox\u2019s proportional hazards model via coordinate descent","volume":"39","author":"Simon","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol., 18.","DOI":"10.1186\/s12874-018-0482-1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Chai, H., and Yang, Y. (2021, January 9\u201312). Multi-Omics Cancer Prognosis Analysis Based on Graph Convolution Network. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Virtual Conference.","DOI":"10.1109\/BIBM52615.2021.9669797"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1177\/0962280216680245","article-title":"Use of the concordance index for predictors of censored survival data","volume":"27","author":"Brentnall","year":"2018","journal-title":"Stat. Methods Med. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1038\/sj.bjc.6601118","article-title":"Survival analysis part I: Basic concepts and first analyses","volume":"89","author":"Clark","year":"2003","journal-title":"Br. J. Cancer"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: An R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"Omics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1038\/nprot.2009.97","article-title":"Mapping identifiers for the integration of genomic datasets with the R\/Bioconductor package biomaRt","volume":"4","author":"Durinck","year":"2009","journal-title":"Nat. Protoc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.molonc.2016.03.008","article-title":"KLK6-regulated miRNA networks activate oncogenic pathways in breast cancer subtypes","volume":"10","author":"Sidiropoulos","year":"2016","journal-title":"Mol. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"777","DOI":"10.21037\/atm-22-2620","article-title":"Bioinformatic analysis of the role of solute carrier-glutamine transporters in breast cancer","volume":"10","author":"Zhao","year":"2022","journal-title":"Ann. Transl. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1002\/cncr.35288","article-title":"Breast cancer therapy in China: Introducing the special collection","volume":"130","author":"Wang","year":"2024","journal-title":"Cancer"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jiao, S., Zhao, D., Zou, Q., Xu, L., Zhang, L., and Su, X. (2022). The characterization of structure and prediction for aquaporin in tumour progression by machine learning. Front. Cell Dev. Biol., 10.","DOI":"10.3389\/fcell.2022.845622"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15144","DOI":"10.1038\/s41598-023-42060-x","article-title":"FAM3 family genes are associated with prognostic value of human cancer: A pan-cancer analysis","volume":"13","author":"Dong","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100304","DOI":"10.1016\/j.jbo.2020.100304","article-title":"ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone","volume":"24","author":"Liu","year":"2020","journal-title":"J. Bone Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1687-4153-2013-8","article-title":"Identification of thresholds for dichotomizing DNA methylation data","volume":"2013","author":"Liu","year":"2013","journal-title":"EURASIP J. Bioinform. Syst. Biol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e8403","DOI":"10.7717\/peerj.8403","article-title":"Differentially expressed genes and key molecules of BRCA1\/2-mutant breast cancer: Evidence from bioinformatics analyses","volume":"8","author":"Li","year":"2020","journal-title":"PeerJ"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"709","DOI":"10.18632\/oncoscience.91","article-title":"Kallikrein-related peptidase 5 induces miRNA-mediated anti-oncogenic pathways in breast cancer","volume":"1","author":"Sidiropoulos","year":"2014","journal-title":"Oncoscience"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, J., Ge, S., Cheng, Y., and Wang, X. (2021). Multi-view spectral clustering based on multi-smooth representation fusion for cancer subtype prediction. Front. Genet., 12.","DOI":"10.3389\/fgene.2021.718915"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e12367","DOI":"10.1002\/2056-4538.12367","article-title":"Somatic mutations in four novel genes contribute to homologous recombination deficiency in breast cancer: A real-world clinical tumor sequencing study","volume":"10","author":"Huang","year":"2024","journal-title":"J. Pathol. Clin. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13058-020-1250-9","article-title":"Prognostic DNA methylation markers for hormone receptor breast cancer: A systematic review","volume":"22","author":"Smits","year":"2020","journal-title":"Breast Cancer Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"18975","DOI":"10.1038\/s41598-022-23646-3","article-title":"Single-nucleus transcriptomics of IDH1-and TP53-mutant glioma stem cells displays diversified commitment on invasive cancer progenitors","volume":"12","author":"Gulaia","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shan, L., Zhu, X., Qiu, H.-Z., Zuo, E.-D., and Cheng, X. (2023). Prognostic significance of TMEM131L in glioma and establishment of oxidative stress prognostic model. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1162394"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1093\/neuonc\/nou161","article-title":"Periostin is a novel therapeutic target that predicts and regulates glioma malignancy","volume":"17","author":"Mikheev","year":"2015","journal-title":"Neuro-Oncol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"152920","DOI":"10.1016\/j.prp.2020.152920","article-title":"Decreased CDKL2 expression is correlated with the progression and poor prognosis of glioma","volume":"216","author":"Yi","year":"2020","journal-title":"Pathol.-Res. Pract."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3407","DOI":"10.18632\/aging.102819","article-title":"Expression and prognostic value of long non-coding RNA H19 in glioma via integrated bioinformatics analyses","volume":"12","author":"Xiao","year":"2020","journal-title":"Aging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"vdab049","DOI":"10.1093\/noajnl\/vdab049","article-title":"An integrative analysis of genome-wide 5-hydroxymethylcytosines in circulating cell-free DNA detects noninvasive diagnostic markers for gliomas","volume":"3","author":"Cai","year":"2021","journal-title":"Neuro-Oncol. Adv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108293","DOI":"10.1016\/j.intimp.2021.108293","article-title":"HOXC6\/8\/10\/13 predict poor prognosis and associate with immune infiltrations in glioblastoma","volume":"101","author":"Yu","year":"2021","journal-title":"Int. Immunopharmacol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4154","DOI":"10.1038\/s41388-019-0714-9","article-title":"Nudt21 regulates the alternative polyadenylation of Pak1 and is predictive in the prognosis of glioblastoma patients","volume":"38","author":"Chu","year":"2019","journal-title":"Oncogene"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7657","DOI":"10.18632\/oncotarget.3150","article-title":"A transcriptomic signature mediated by HOXA9 promotes human glioblastoma initiation, aggressiveness and resistance to temozolomide","volume":"6","author":"Pojo","year":"2015","journal-title":"Oncotarget"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e1366","DOI":"10.1002\/hsr2.1366","article-title":"Impact of propofol versus sevoflurane anesthesia on molecular subtypes and immune checkpoints of glioma during surgery","volume":"6","author":"Cen","year":"2023","journal-title":"Health Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5143","DOI":"10.7150\/thno.27310","article-title":"Overexpression of HOXC10 promotes angiogenesis in human glioma via interaction with PRMT5 and upregulation of VEGFA expression","volume":"8","author":"Tan","year":"2018","journal-title":"Theranostics"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s12032-010-9660-7","article-title":"Expressions of matrix metalloproteinase-7 and matrix metalloproteinase-14 associated with the activation of extracellular signal-regulated kinase1\/2 in human brain gliomas of different pathological grades","volume":"28","author":"Xie","year":"2011","journal-title":"Med. Oncol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e18352","DOI":"10.1111\/jcmm.18352","article-title":"A potassium-chloride co-transporter with altered genome architecture functions as a suppressor in glioma","volume":"28","author":"Liu","year":"2024","journal-title":"J. Cell. Mol. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1038\/nrc3245","article-title":"The immune contexture in human tumours: Impact on clinical outcome","volume":"12","author":"Fridman","year":"2012","journal-title":"Nat. Rev. Cancer"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"e20190297","DOI":"10.1084\/jem.20190297","article-title":"The role of interleukin-17 in tumor development and progression","volume":"217","author":"Zhao","year":"2019","journal-title":"J. Exp. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1158\/1078-0432.CCR-06-2221","article-title":"Nuclear factor-kappaB in development, prevention, and therapy of cancer","volume":"13","author":"Waes","year":"2007","journal-title":"Clin. Cancer Res."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, B., Zhang, X., Cao, Q., Chen, J., Lin, C., Xiang, T., and Zeng, P. (2022). Construction and validation of a prognostic risk model for breast cancer based on protein expression. BMC Med. Genom., 15.","DOI":"10.1186\/s12920-022-01299-5"}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/6\/597\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T12:24:11Z","timestamp":1779452651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/6\/597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,22]]},"references-count":59,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["genes17060597"],"URL":"https:\/\/doi.org\/10.3390\/genes17060597","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,22]]}}}