Skip to content
2000
image of Molecular Subtypes of Mixed Gastric Cancer Defined by Machine Learning for Predicting Prognosis and Treatment Response

Abstract

Background

Gastric cancer (GC) is traditionally classified into intestinal (IGC), diffuse (DGC), and mixed (MGC) types based on pathological features, with each subtype exhibiting distinct clinical outcomes. Among these, DGC is associated with poor prognosis, characterized by low cell adhesion and a high stromal component. Recent proteomic studies have revealed significant differences in extracellular matrix (ECM) composition between DGC and IGC, highlighting the critical role of ECM in tumor biology. MGC, which combines both intestinal and diffuse characteristics, presents substantial heterogeneity, complicating prognosis and personalized treatment approaches. This study reclassifies MGC using extracellular matrix receptor (ECMR) and cell adhesion (CA)-related genes (ECRGs), closely linked to the biological behavior of DGC, to provide insights into prognosis and treatment response.

Methods

RNA sequencing data and clinical information from GC patients were collected from the TCGA and GEO databases, excluding cases of pure IGC and DGC. Based on ECMR and CA-related genes, supervised clustering non-negative Matrix Factorization (NMF) was used to identify molecular subtypes in MGC. Differential expression and Cox regression analyses were performed to identify prognostic genes, and an ECMR and CA-based gene signature (ECRS) was developed using machine learning techniques. Gene Set Variation Analysis (GSVA) was conducted to assess functional differences between risk groups, while TIDE and pRRophetic analyses were used to predict responses to immunotherapy and chemotherapy.

Results

A total of 239 MGC patients were classified into two molecular subtypes with significant differences in prognosis. Subtype 2 displayed distinct ECM interactions and connective tissue development pathways. To refine the ECRS model, we tested 117 model combinations across 10 machine learning algorithms, selecting the configuration with the best predictive accuracy. This optimized model distinguished biological and immune characteristics between high- and low-risk groups, with low-risk patients showing greater sensitivity to immunotherapy and standard chemotherapy.

Conclusion

This study identifies novel molecular subtypes of MGC based on ECMR and CA-related genes and establishes an effective ECRS model to predict prognosis, immunotherapy response, and chemotherapy sensitivity. This model supports personalized treatment strategies for MGC.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673376656250602095713
2025-06-23
2025-09-07

  • From This Site

    /content/journals/cmc/10.2174/0109298673376656250602095713
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Guan W.L. He Y. Xu R.H. Gastric cancer treatment: Recent progress and future perspectives. J. Hematol. Oncol. 2023 16 1 57 10.1186/s13045‑023‑01451‑3 37245017
    [Google Scholar]
  2. Joshi S.S. Badgwell B.D. Current treatment and recent progress in gastric cancer. CA Cancer J. Clin. 2021 71 3 264 279 10.3322/caac.21657 33592120
    [Google Scholar]
  3. Vitale I. Shema E. Loi S. Galluzzi L. Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nat. Med. 2021 27 2 212 224 10.1038/s41591‑021‑01233‑9 33574607
    [Google Scholar]
  4. Jia Q. Wang A. Yuan Y. Zhu B. Long H. Heterogeneity of the tumor immune microenvironment and its clinical relevance. Exp. Hematol. Oncol. 2022 11 1 24 10.1186/s40164‑022‑00277‑y 35461288
    [Google Scholar]
  5. Laurén P. The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma. Acta Pathol. Microbiol. Scand. 1965 64 1 31 49 10.1111/apm.1965.64.1.31 14320675
    [Google Scholar]
  6. Zhang M. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut 2021 70 3 464 475 10.1136/gutjnl‑2019‑320368
    [Google Scholar]
  7. Bian S. Wang Y. Zhou Y. Wang W. Guo L. Wen L. Fu W. Zhou X. Tang F. Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer. Natl. Sci. Rev. 2023 10 6 nwad094 10.1093/nsr/nwad094 37347037
    [Google Scholar]
  8. Li R. Zhang H. Cao Y. Liu X. Chen Y. Qi Y. Wang J. Yu K. Lin C. Liu H. He H. Li H. Chen L. Shen Z. Qin J. Zhang W. Sun Y. Xu J. Lauren classification identifies distinct prognostic value and functional status of intratumoral CD8+ T cells in gastric cancer. Cancer Immunol. Immunother. 2020 69 7 1327 1336 10.1007/s00262‑020‑02550‑7 32200421
    [Google Scholar]
  9. Jiménez Fonseca P. Carmona-Bayonas A. Hernández R. Custodio A. Cano J.M. Lacalle A. Echavarria I. Macias I. Mangas M. Visa L. Buxo E. Álvarez Manceñido F. Viudez A. Pericay C. Azkarate A. Ramchandani A. López C. Martinez de Castro E. Fernández Montes A. Longo F. Sánchez Bayona R. Limón M.L. Diaz-Serrano A. Martin Carnicero A. Arias D. Cerdà P. Rivera F. Vieitez J.M. Sánchez Cánovas M. Garrido M. Gallego J. Lauren subtypes of advanced gastric cancer influence survival and response to chemotherapy: Real-world data from the AGAMENON national cancer registry. Br. J. Cancer 2017 117 6 775 782 10.1038/bjc.2017.245 28765618
    [Google Scholar]
  10. Petrelli F. Berenato R. Turati L. Mennitto A. Steccanella F. Caporale M. Dallera P. de Braud F. Pezzica E. Bartolomeo M.D. Sgroi G. Mazzaferro V. Pietrantonio F. Barni S. Prognostic value of diffuse versus intestinal histotype in patients with gastric cancer: A systematic review and meta-analysis. J. Gastrointest. Oncol. 2017 8 1 148 163 10.21037/jgo.2017.01.10 28280619
    [Google Scholar]
  11. Tang C.T. Zeng L. Yang J. Zeng C. Chen Y. Analysis of the incidence and survival of gastric cancer based on the lauren classification: A large population-based study using SEER. Front. Oncol. 2020 10 1212 10.3389/fonc.2020.01212 32850357
    [Google Scholar]
  12. Ma J. Shen H. Kapesa L. Zeng S. Lauren classification and individualized chemotherapy in gastric cancer. Oncol. Lett. 2016 11 5 2959 2964 10.3892/ol.2016.4337 27123046
    [Google Scholar]
  13. Wang K. Yu Y. Zhao J. Meng Q. Xu C. Ren J. Zhang Y. Wang Y. Wang G. A retrospective analysis of the lauren classification in the choice of XELOX or SOX as an adjuvant chemotherapy for gastric cancer. Curr. Gene Ther. 2024 24 2 147 158 10.2174/0115665232247694230921060213 37767800
    [Google Scholar]
  14. Al-Batran S.E. Homann N. Pauligk C. Goetze T.O. Meiler J. Kasper S. Kopp H.G. Mayer F. Haag G.M. Luley K. Lindig U. Schmiegel W. Pohl M. Stoehlmacher J. Folprecht G. Probst S. Prasnikar N. Fischbach W. Mahlberg R. Trojan J. Koenigsmann M. Martens U.M. Thuss-Patience P. Egger M. Block A. Heinemann V. Illerhaus G. Moehler M. Schenk M. Kullmann F. Behringer D.M. Heike M. Pink D. Teschendorf C. Löhr C. Bernhard H. Schuch G. Rethwisch V. von Weikersthal L.F. Hartmann J.T. Kneba M. Daum S. Schulmann K. Weniger J. Belle S. Gaiser T. Oduncu F.S. Güntner M. Hozaeel W. Reichart A. Jäger E. Kraus T. Mönig S. Bechstein W.O. Schuler M. Schmalenberg H. Hofheinz R.D. FLOT4-AIO Investigators Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel versus fluorouracil or capecitabine plus cisplatin and epirubicin for locally advanced, resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4): A randomised, phase 2/3 trial. Lancet 2019 393 10184 1948 1957 10.1016/S0140‑6736(18)32557‑1 30982686
    [Google Scholar]
  15. Al-Batran S.E. Hofheinz R.D. Pauligk C. Kopp H.G. Haag G.M. Luley K.B. Meiler J. Homann N. Lorenzen S. Schmalenberg H. Probst S. Koenigsmann M. Egger M. Prasnikar N. Caca K. Trojan J. Martens U.M. Block A. Fischbach W. Mahlberg R. Clemens M. Illerhaus G. Zirlik K. Behringer D.M. Schmiegel W. Pohl M. Heike M. Ronellenfitsch U. Schuler M. Bechstein W.O. Königsrainer A. Gaiser T. Schirmacher P. Hozaeel W. Reichart A. Goetze T.O. Sievert M. Jäger E. Mönig S. Tannapfel A. Histopathological regression after neoadjuvant docetaxel, oxaliplatin, fluorouracil, and leucovorin versus epirubicin, cisplatin, and fluorouracil or capecitabine in patients with resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4-AIO): Results from the phase 2 part of a multicentre, open-label, randomised phase 2/3 trial. Lancet Oncol. 2016 17 12 1697 1708 10.1016/S1470‑2045(16)30531‑9 27776843
    [Google Scholar]
  16. Shi W. Wang Y. Xu C. Li Y. Ge S. Bai B. Zhang K. Wang Y. Zheng N. Wang J. Wang S. Ji G. Li J. Nie Y. Liang W. Wu X. Cui J. Wang Y. Chen L. Zhao Q. Shen L. He F. Qin J. Ding C. Multilevel proteomic analyses reveal molecular diversity between diffuse-type and intestinal-type gastric cancer. Nat. Commun. 2023 14 1 835 10.1038/s41467‑023‑35797‑6 36788224
    [Google Scholar]
  17. Nakamura Y. Kawazoe A. Lordick F. Janjigian Y.Y. Shitara K. Biomarker-targeted therapies for advanced-stage gastric and gastro-oesophageal junction cancers: An emerging paradigm. Nat. Rev. Clin. Oncol. 2021 18 8 473 487 10.1038/s41571‑021‑00492‑2 33790428
    [Google Scholar]
  18. Pyo J.H. Ahn S. Lee H. Min B.H. Lee J.H. Shim S.G. Choi M.G. Lee J.H. Sohn T.S. Bae J.M. Kim K.M. Yeon S. Jung S.H. Kim J.J. Kim S. Clinicopathological features and prognosis of mixed-type t1a gastric cancer based on lauren’s classification. Ann. Surg. Oncol. 2016 23 S5 784 791 10.1245/s10434‑016‑5549‑9 27613552
    [Google Scholar]
  19. Men X. Shi X. Xu Q. Liu M. Yang H. Wang L. Men X. Xu H. Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis. Medicine 2024 103 16 e37798 10.1097/MD.0000000000037798 38640295
    [Google Scholar]
  20. Dai J. Gao J. Dong H. Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma. Aging 2024 16 14 11162 11184 10.18632/aging.205952 39012280
    [Google Scholar]
  21. Lou X. Wei C. Research progress of machine learning in mining of tumor markers. Chin. J. Lab. Med. 2021 44 532 536
    [Google Scholar]
  22. Sharma A. Lysenko A. Jia S. Boroevich K.A. Tsunoda T. Advances in AI and machine learning for predictive medicine. J. Hum. Genet. 2024 69 10 487 497 10.1038/s10038‑024‑01231‑y 38424184
    [Google Scholar]
  23. Sundar R. Barr Kumarakulasinghe N. Huak Chan Y. Yoshida K. Yoshikawa T. Miyagi Y. Rino Y. Masuda M. Guan J. Sakamoto J. Tanaka S. Tan A.L.K. Hoppe M.M. Jeyasekharan A.D. Ng C.C.Y. De Simone M. Grabsch H.I. Lee J. Oshima T. Tsuburaya A. Tan P. Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: Results from the randomised phase III SAMIT trial. Gut 2022 71 4 676 685 10.1136/gutjnl‑2021‑324060 33980610
    [Google Scholar]
  24. Su Y. Tian X. Gao R. Guo W. Chen C. Chen C. Jia D. Li H. Lv X. Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Comput. Biol. Med. 2022 145 105409 10.1016/j.compbiomed.2022.105409 35339846
    [Google Scholar]
  25. Skubleny D. Purich K. McLean D.R. Martins-Filho S.N. Buttenschoen K. Haase E. McCall M. Ghosh S. Spratlin J.L. Schiller D.E. Rayat G.R. The tumor immune microenvironment drives survival outcomes and therapeutic response in an integrated molecular analysis of gastric adenocarcinoma. Clin. Cancer Res. 2024 30 23 5385 5398 10.1158/1078‑0432.CCR‑23‑3523 39325010
    [Google Scholar]
  26. Leek J.T. Johnson W.E. Parker H.S. Jaffe A.E. Storey J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012 28 6 882 883 10.1093/bioinformatics/bts034 22257669
    [Google Scholar]
  27. Hamamoto R. Takasawa K. Machino H. Kobayashi K. Takahashi S. Bolatkan A. Shinkai N. Sakai A. Aoyama R. Yamada M. Asada K. Komatsu M. Okamoto K. Kameoka H. Kaneko S. Application of non-negative matrix factorization in oncology: One approach for establishing precision medicine. Brief. Bioinform. 2022 23 4 bbac246 10.1093/bib/bbac246 35788277
    [Google Scholar]
  28. Liu H. Zhang W. Zhang Y. Adegboro A.A. Fasoranti D.O. Dai L. Pan Z. Liu H. Xiong Y. Li W. Peng K. Wanggou S. Li X. Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection. Comput. Struct. Biotechnol. J. 2024 23 2798 2810 10.1016/j.csbj.2024.06.035 39055398
    [Google Scholar]
  29. 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. IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol. 2021 12 687975 10.3389/fimmu.2021.687975 34276676
    [Google Scholar]
  30. Jiang P. Gu S. Pan D. Fu J. Sahu A. Hu X. Li Z. Traugh N. Bu X. Li B. Liu J. Freeman G.J. Brown M.A. Wucherpfennig K.W. Liu X.S. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 2018 24 10 1550 1558 10.1038/s41591‑018‑0136‑1 30127393
    [Google Scholar]
  31. Geeleher P. Cox N. Huang R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014 9 9 e107468 10.1371/journal.pone.0107468 25229481
    [Google Scholar]
  32. Kim R. An M. Lee H. Mehta A. Heo Y.J. Kim K.M. Lee S.Y. Moon J. Kim S.T. Min B.H. Kim T.J. Rha S.Y. Kang W.K. Park W.Y. Klempner S.J. Lee J. Early tumor–immune microenvironmental remodeling and response to first-line fluoropyrimidine and platinum chemotherapy in advanced gastric cancer. Cancer Discov. 2022 12 4 984 1001 10.1158/2159‑8290.CD‑21‑0888 34933901
    [Google Scholar]
  33. Díaz Del Arco C. Ortega Medina L. Estrada Muñoz L. García Gómez de Las Heras S. Fernández Aceñero M.J. Is there still a place for conventional histopathology in the age of molecular medicine? Laurén classification, inflammatory infiltration and other current topics in gastric cancer diagnosis and prognosis. Histol. Histopathol. 2021 36 6 587 613 33565601
    [Google Scholar]
  34. Veldhuizen G.P. Röcken C. Behrens H.M. Cifci D. Muti H.S. Yoshikawa T. Arai T. Oshima T. Tan P. Ebert M.P. Pearson A.T. Calderaro J. Grabsch H.I. Kather J.N. Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: A multi-institutional retrospective study. Gastric Cancer 2023 26 5 708 720 10.1007/s10120‑023‑01398‑x 37269416
    [Google Scholar]
  35. Wu L.W. Jang S.J. Shapiro C. Fazlollahi L. Wang T.C. Ryeom S.W. Moy R.H. Diffuse gastric cancer: A comprehensive review of molecular features and emerging therapeutics. Target. Oncol. 2024 19 6 845 865 10.1007/s11523‑024‑01097‑2 39271577
    [Google Scholar]
  36. Becker K.F. Atkinson M.J. Reich U. Becker I. Nekarda H. Siewert J.R. Höfler H. E-cadherin gene mutations provide clues to diffuse type gastric carcinomas. Cancer Res. 1994 54 14 3845 3852 8033105
    [Google Scholar]
  37. Li Y. Xu C. Wang B. Xu F. Ma F. Qu Y. Jiang D. Li K. Feng J. Tian S. Wu X. Wang Y. Liu Y. Qin Z. Liu Y. Qin J. Song Q. Zhang X. Sujie A. Huang J. Liu T. Shen K. Zhao J.Y. Hou Y. Ding C. Author Correction: Proteomic characterization of gastric cancer response to chemotherapy and targeted therapy reveals potential therapeutic strategies. Nat. Commun. 2022 13 1 6749 10.1038/s41467‑022‑34238‑0 36347856
    [Google Scholar]
  38. Perrot-Applanat M. Vacher S. Pimpie C. Chemlali W. Derieux S. Pocard M. Bieche I. Differential gene expression in growth factors, epithelial mesenchymal transition and chemotaxis in the diffuse type compared with the intestinal type of gastric cancer. Oncol. Lett. 2019 18 1 674 686 10.3892/ol.2019.10392 31289541
    [Google Scholar]
  39. Strilic B. Offermanns S. Intravascular survival and extravasation of tumor cells. Cancer Cell 2017 32 3 282 293 10.1016/j.ccell.2017.07.001 28898694
    [Google Scholar]
  40. Cooper J. Giancotti F.G. Integrin signaling in cancer: Mechanotransduction, stemness, epithelial plasticity, and therapeutic resistance. Cancer Cell 2019 35 3 347 367 10.1016/j.ccell.2019.01.007 30889378
    [Google Scholar]
  41. Läubli H. Borsig L. Altered cell adhesion and glycosylation promote cancer immune suppression and metastasis. Front. Immunol. 2019 10 2120 10.3389/fimmu.2019.02120 31552050
    [Google Scholar]
  42. Fawcett J. Harris A.L. Cell adhesion molecules and cancer. Curr. Opin. Oncol. 1992 4 1 142 148 10.1097/00001622‑199202000‑00019 1591286
    [Google Scholar]
  43. Venhuizen J.H. Jacobs F.J.C. Span P.N. Zegers M.M. P120 and E-cadherin: Double-edged swords in tumor metastasis. Semin. Cancer Biol. 2020 60 107 120 10.1016/j.semcancer.2019.07.020 31369816
    [Google Scholar]
  44. Fan C. Xiong F. Zhang S. Gong Z. Liao Q. Li G. Guo C. Xiong W. Huang H. Zeng Z. Role of adhesion molecules in cancer and targeted therapy. Sci. China Life Sci. 2024 67 5 940 957 10.1007/s11427‑023‑2417‑3 38212458
    [Google Scholar]
  45. Harjunpää H. Llort Asens M. Guenther C. Fagerholm S.C. Cell adhesion molecules and their roles and regulation in the immune and tumor microenvironment. Front. Immunol. 2019 10 1078 10.3389/fimmu.2019.01078 31231358
    [Google Scholar]
  46. Nakayama I. Qi C. Chen Y. Nakamura Y. Shen L. Shitara K. Claudin 18.2 as a novel therapeutic target. Nat. Rev. Clin. Oncol. 2024 21 5 354 369 10.1038/s41571‑024‑00874‑2 38503878
    [Google Scholar]
  47. Winkler J. Abisoye-Ogunniyan A. Metcalf K.J. Werb Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 2020 11 1 5120 10.1038/s41467‑020‑18794‑x 33037194
    [Google Scholar]
  48. Prakash J. Shaked Y. The interplay between extracellular matrix remodeling and cancer therapeutics. Cancer Discov. 2024 14 8 1375 1388 10.1158/2159‑8290.CD‑24‑0002 39091205
    [Google Scholar]
  49. Naba A. Mechanisms of assembly and remodelling of the extracellular matrix. Nat. Rev. Mol. Cell Biol. 2024 25 11 865 885 10.1038/s41580‑024‑00767‑3 39223427
    [Google Scholar]
  50. Yuan Z. Li Y. Zhang S. Wang X. Dou H. Yu X. Zhang Z. Yang S. Xiao M. Extracellular matrix remodeling in tumor progression and immune escape: From mechanisms to treatments. Mol. Cancer 2023 22 1 48 10.1186/s12943‑023‑01744‑8 36906534
    [Google Scholar]
  51. Bonnans C. Chou J. Werb Z. Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 2014 15 12 786 801 10.1038/nrm3904 25415508
    [Google Scholar]
  52. Sleeboom J.J.F. van Tienderen G.S. Schenke-Layland K. van der Laan L.J.W. Khalil A.A. Verstegen M.M.A. The extracellular matrix as hallmark of cancer and metastasis: From biomechanics to therapeutic targets. Sci. Transl. Med. 2024 16 728 eadg3840 10.1126/scitranslmed.adg3840 38170791
    [Google Scholar]
  53. Neijzen D. Lunter G. Unsupervised learning for medical data: A review of probabilistic factorization methods. Stat. Med. 2023 42 30 5541 5554 10.1002/sim.9924 37850249
    [Google Scholar]
  54. Zhang B. Liu M. Mai F. Li X. Wang W. Huang Q. Du X. Ding W. Li Y. Barwick B.G. Ni J.J. Osunkoya A.O. Chen Y. Zhou W. Xia S. Dong J.T. Interruption of KLF5 acetylation promotes PTEN-deficient prostate cancer progression by reprogramming cancer-associated fibroblasts. J. Clin. Invest. 2024 134 14 e175949 10.1172/JCI175949 38781024
    [Google Scholar]
  55. Wu Y. Chen S. Shao Y. Su Y. Li Q. Wu J. Zhu J. Wen H. Huang Y. Zheng Z. Chen X. Ju X. Huang S. Wu X. Hu Z. KLF5 promotes tumor progression and parp inhibitor resistance in ovarian cancer. Adv. Sci. 2023 10 31 2304638 10.1002/advs.202304638 37702443
    [Google Scholar]
  56. Qin J. Zhou Z. Chen W. Wang C. Zhang H. Ge G. Shao M. You D. Fan Z. Xia H. Liu R. Chen C. BAP1 promotes breast cancer cell proliferation and metastasis by deubiquitinating KLF5. Nat. Commun. 2015 6 1 8471 10.1038/ncomms9471 26419610
    [Google Scholar]
  57. Wu Q. Liu Z. Gao Z. Luo Y. Li F. Yang C. Wang T. Meng X. Chen H. Li J. Kong Y. Dong C. Sun S. Chen C. KLF5 inhibition potentiates anti-PD1 efficacy by enhancing CD8 + T-cell-dependent antitumor immunity. Theranostics 2023 13 4 1381 1400 10.7150/thno.82182 36923542
    [Google Scholar]
  58. Zhang Z. Xu H. He J. Hu Q. Liu Y. Xu Z. Lou W. Wu W. Zhang L. Pu N. Shi C. Xu Y. Wang W. Liu L. Inhibition of KLF5 promotes ferroptosis via the ZEB1/HMOX1 axis to enhance sensitivity to oxaliplatin in cancer cells. Cell Death Dis. 2025 16 1 28 10.1038/s41419‑025‑07330‑8 39827156
    [Google Scholar]
  59. Zhang B. Li Y. Wu Q. Xie L. Barwick B. Fu C. Li X. Wu D. Xia S. Chen J. Qian W.P. Yang L. Osunkoya A.O. Boise L. Vertino P.M. Zhao Y. Li M. Chen H.R. Kowalski J. Kucuk O. Zhou W. Dong J.T. Acetylation of KLF5 maintains EMT and tumorigenicity to cause chemoresistant bone metastasis in prostate cancer. Nat. Commun. 2021 12 1 1714 10.1038/s41467‑021‑21976‑w 33731701
    [Google Scholar]
  60. Wu L. Liu X. Lei J. Zhang N. Zhao H. Zhang J. Deng H. Li Y. Fibrinogen-like protein 2 promotes tumor immune suppression by regulating cholesterol metabolism in myeloid-derived suppressor cells. J. Immunother. Cancer 2023 11 12 e008081 10.1136/jitc‑2023‑008081 38056898
    [Google Scholar]
  61. Huang W. Han Z. Sun Z. Feng H. Zhao L. Yuan Q. Chen C. Yu S. Hu Y. Yu J. Liu H. Li G. Jiang Y. PAK6 promotes homologous-recombination to enhance chemoresistance to oxaliplatin through ATR/CHK1 signaling in gastric cancer. Cell Death Dis. 2022 13 7 658 10.1038/s41419‑022‑05118‑8 35902562
    [Google Scholar]
  62. Fram S. King H. Sacks D.B. Wells C.M. A PAK6–IQGAP1 complex promotes disassembly of cell–cell adhesions. Cell. Mol. Life Sci. 2014 71 14 2759 2773 10.1007/s00018‑013‑1528‑5 24352566
    [Google Scholar]
  63. Joyce J.A. Fearon D.T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 2015 348 6230 74 80 10.1126/science.aaa6204 25838376
    [Google Scholar]
  64. Coati I. Lotz G. Fanelli G.N. Brignola S. Lanza C. Cappellesso R. Pellino A. Pucciarelli S. Spolverato G. Guzzardo V. Munari G. Zaninotto G. Scarpa M. Mastracci L. Farinati F. Realdon S. Pilati P. Lonardi S. Valeri N. Rugge M. Kiss A. Loupakis F. Fassan M. Claudin-18 expression in oesophagogastric adenocarcinomas: A tissue microarray study of 523 molecularly profiled cases. Br. J. Cancer 2019 121 3 257 263 10.1038/s41416‑019‑0508‑4 31235864
    [Google Scholar]
  65. Xiao Z. Todd L. Huang L. Noguera-Ortega E. Lu Z. Huang L. Kopp M. Li Y. Pattada N. Zhong W. Guo W. Scholler J. Liousia M. Assenmacher C.A. June C.H. Albelda S.M. Puré E. Desmoplastic stroma restricts T cell extravasation and mediates immune exclusion and immunosuppression in solid tumors. Nat. Commun. 2023 14 1 5110 10.1038/s41467‑023‑40850‑5 37607999
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673376656250602095713
Loading
Molecular Subtypes of Mixed Gastric Cancer Defined by Machine Learning for Predicting Prognosis and Treatment Response
Bentham Science Publishers ; https://doi.org/10.2174/0109298673376656250602095713
/content/journals/cmc/10.2174/0109298673376656250602095713
/content/journals/cmc/10.2174/0109298673376656250602095713
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: chemotherapy ; immunotherapy ; prognosis ; prognostic genes ; Mixed-type gastric cancer ; machine learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test