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image of Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study

Abstract

Background

PANoptosis, a novelty mechanism of cell death involving crosstalk between apoptosis, pyroptosis, and necroptosis, is strongly associated with tumor cell death and immunotherapy efficacy. However, its relevance in lung adenocarcinoma (LUAD) remains to be elucidated.

Methods

In this study, we acquired 18 PANoptosis-related differentially expressed gene (PRDEG) of LUAD. Based on these genes, LUAD samples were identified with different subtypes by unsupervised clustering. Next, we compared the differences between the subtypes, including clinical features, immune microenvironment, and potentially sensitive drugs. Furthermore, we used machine learning to identify hub prognostic PRDEGs, construct a risk score, and validate it on other external datasets. We incorporated the patient's clinical information and risk score into the proportional hazards model and lasso-cox models to find key prognostic features and constructed five prognostic models. The best model was identified the area under the curve and validated on an external dataset.

Results

LUAD patients were divided into two clusters named C1 and C2, respectively. The C2 cluster exhibited shorter survival time, more advanced tumor stage, higher suppressive immune cell scores, such as dendritic cells, and higher expression of inhibitory immune checkpoints, such as LAG3 and CD86. TIMP1, CAV1, and CD69 were recognized as key prognostic factors, and risk scores predicted survival with significant differences in the external validation set. Risk score and N-stage were identified as critical prognostic features. The Coxph model outperformed other machine learning clinical models. The 1-, 3-, and 5-year time-ROCs in the external validation set were 0.55, 0.59, and 0.60, respectively.

Conclusion

We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of patients with LUAD as well as the tumor microenvironment.

© 2024 Bentham Science Publishers
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2024-10-14
2025-05-13

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References

  1. Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020. CA Cancer J. Clin. 2020 70 1 7 30 10.3322/caac.21590 31912902
    [Google Scholar]
  2. Kordiak J. Bielec F. Jabłoński S. Pastuszak-Lewandoska D. Role of Beta-Carotene in Lung Cancer Primary Chemopre-vention: A Systematic Review with Meta-Analysis and Meta-Regression. Nutrients 2022 14 7 1361 10.3390/nu14071361 35405977
    [Google Scholar]
  3. Siegel R.L. Miller K.D. Wagle N.S. Jemal A. Cancer statis-tics, 2023. CA Cancer J. Clin. 2023 73 1 17 48 10.3322/caac.21763 36633525
    [Google Scholar]
  4. Shukla S. Evans J.R. Malik R. Feng F.Y. Dhanasekaran S.M. Cao X. Chen G. Beer D.G. Jiang H. Chinnaiyan A.M. Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma. J. Natl. Cancer Inst. 2017 109 1 djw200 10.1093/jnci/djw200 27707839
    [Google Scholar]
  5. Rizzo A. Identifying optimal first-line treatment for advanced non-small cell lung carcinoma with high PD-L1 expression: a matter of debate. Br. J. Cancer 2022 127 8 1381 1382 10.1038/s41416‑022‑01929‑w 36064585
    [Google Scholar]
  6. Rizzo A. Mollica V. Tateo V. Tassinari E. Marchetti A. Rosellini M. De Luca R. Santoni M. Massari F. Hyper-transaminasemia in cancer patients receiving immunotherapy and immune-based combinations: the MOUSEION-05 study. Cancer Immunol. Immunother. 2023 72 6 1381 1394 10.1007/s00262‑023‑03366‑x 36695827
    [Google Scholar]
  7. Dall’Olio F.G. Rizzo A. Mollica V. Massucci M. Maggio I. Massari F. Immortal time bias in the association between toxicity and response for immune checkpoint inhibitors: a meta-analysis. Immunotherapy 2021 13 3 257 270 10.2217/imt‑2020‑0179 33225800
    [Google Scholar]
  8. Guven D.C. Sahin T.K. Erul E. Rizzo A. Ricci A.D. Aksoy S. Yalcin S. The association between albumin levels and survival in patients treated with immune checkpoint in-hibitors: A systematic review and meta-analysis. Front. Mol. Biosci. 2022 9 1039121 10.3389/fmolb.2022.1039121 36533070
    [Google Scholar]
  9. Yang Z. Kao X. Huang N. Yuan K. Chen J. He M. Identification and Analysis of PANoptosis-Related Genes in Sepsis-Induced Lung Injury by Bioinformatics and Experi-mental Verification. J. Inflamm. Res. 2024 17 1941 1956 10.2147/JIR.S452608 38562657
    [Google Scholar]
  10. Wang Y. Kanneganti T.D. From pyroptosis, apoptosis and necroptosis to PANoptosis: A mechanistic compendium of programmed cell death pathways. Comput. Struct. Biotechnol. J. 2021 19 4641 4657 10.1016/j.csbj.2021.07.038 34504660
    [Google Scholar]
  11. Huang J. Jiang S. Liang L. He H. Liu Y. Cong L. Jiang Y. Analysis of PANoptosis-Related LncRNA-miRNA-mRNA Network Reveals LncRNA SNHG7 Involved in Chemo-Resistance in Colon Adenocarcinoma. Front. Oncol. 2022 12 888105 10.3389/fonc.2022.888105 35646635
    [Google Scholar]
  12. Zhang Z. Zhang F. Pang P. Li Y. Chen X. Sun S. Bian Y. Identification of PANoptosis-relevant subgroups to evalu-ate the prognosis and immune landscape of patients with liver hepatocellular carcinoma. Front. Cell Dev. Biol. 2023 11 1210456 10.3389/fcell.2023.1210456 37325556
    [Google Scholar]
  13. Yang P. Huang G. Li Y. Yu L. Yin Z. Li Q. Identifica-tion of PANoptosis-related biomarkers and analysis of prog-nostic values in head and neck squamous cell carcinoma. Sci. Rep. 2024 14 1 9824 10.1038/s41598‑024‑60441‑8 38684755
    [Google Scholar]
  14. Wang J.M. Yang J. Xia W.Y. Wang Y.M. Zhu Y.B. Huang Q. Feng T. Xie L.S. Li S.H. Liu S.Q. Yu S.G. Wu Q.F. Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis. Int. J. Mol. Sci. 2023 25 1 348 10.3390/ijms25010348 38203518
    [Google Scholar]
  15. Andreasson J. Bodén E. Fakhro M. von Wachter C. Olm F. Malmsjö M. Hallgren O. Lindstedt S. Exhaled phos-pholipid transfer protein and hepatocyte growth factor recep-tor in lung adenocarcinoma. Respir. Res. 2022 23 1 369 10.1186/s12931‑022‑02302‑4 36544145
    [Google Scholar]
  16. Zhong H. Wang J. Zhu Y. Shen Y. Comprehensive Anal-ysis of a Nine-Gene Signature Related to Tumor Microenvi-ronment in Lung Adenocarcinoma. Front. Cell Dev. Biol. 2021 9 700607 10.3389/fcell.2021.700607 34540825
    [Google Scholar]
  17. Zhu H. Yue H. Xie Y. Chen B. Zhou Y. Liu W. Bioin-formatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma. J. Thorac. Dis. 2021 13 2 1172 1186 10.21037/jtd‑21‑49 33717590
    [Google Scholar]
  18. Zhou C. Wang Y. Lei L. Ji M.H. Yang J.J. Xia H. Iden-tifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction. Front. Mol. Biosci. 2020 7 563142 10.3389/fmolb.2020.563142 33195410
    [Google Scholar]
  19. Huang J. Zhang J. Zhang F. Lu S. Guo S. Shi R. Zhai Y. Gao Y. Tao X. Jin Z. You L. Wu J. Identification of a disulfidptosis-related genes signature for prognostic impli-cation in lung adenocarcinoma. Comput. Biol. Med. 2023 165 107402 10.1016/j.compbiomed.2023.107402 37657358
    [Google Scholar]
  20. He R. Zuo S. A Robust 8-Gene Prognostic Signature for Early-Stage Non-small Cell Lung Cancer. Front. Oncol. 2019 9 693 10.3389/fonc.2019.00693 31417870
    [Google Scholar]
  21. Rosamaria P. Daniela P. Rosanna L. Michele M. An-namaria C. Pamela P. Antonietta B.M. Alfredo Z.F. Ga-briella D.B. Antonia Z. Stefania T. Simona D.S. KRAS-Driven Lung Adenocarcinoma and B Cell Infiltration: Novel Insights for Immunotherapy. Cancers (Basel) 2019 11 8 1145 10.3390/cancers11081145 31405063
    [Google Scholar]
  22. He D. Tang H. Yang X. Liu X. Zhang Y. Shi J. Elabo-ration and validation of a prognostic signature associated with disulfidoptosis in lung adenocarcinoma, consolidated with in-tegration of single-cell RNA sequencing and bulk RNA se-quencing techniques. Front. Immunol. 2023 14 1278496 10.3389/fimmu.2023.1278496 37965333
    [Google Scholar]
  23. Maeser D. Gruener R.F. Huang R.S. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief. Bioinform. 2021 22 6 bbab260 10.1093/bib/bbab260 34260682
    [Google Scholar]
  24. Jiang M. Qi L. Li L. Wu Y. Song D. Li Y. Caspase‐8: A key protein of cross‐talk signal way in “ PANOPTOSIS ” in cancer. Int. J. Cancer 2021 149 7 1408 1420 10.1002/ijc.33698 34028029
    [Google Scholar]
  25. Malireddi R.K.S. Karki R. Sundaram B. Kancharana B. Lee S. Samir P. Kanneganti T.D. Inflammatory Cell Death, PANoptosis, Mediated by Cytokines in Diverse Cancer Line-ages Inhibits Tumor Growth. Immunohorizons 2021 5 7 568 580 10.4049/immunohorizons.2100059 34290111
    [Google Scholar]
  26. Karki R. Lee S. Mall R. Pandian N. Wang Y. Sharma B.R. Malireddi R.K.S. Yang D. Trifkovic S. Steele J.A. Connelly J.P. Vishwanath G. Sasikala M. Reddy D.N. Vogel P. Pruett-Miller S.M. Webby R. Jonsson C.B. Kanneganti T.D. ZBP1-dependent inflammatory cell death, PANoptosis, and cytokine storm disrupt IFN therapeutic effi-cacy during coronavirus infection. Sci. Immunol. 2022 7 74 eabo6294 10.1126/sciimmunol.abo6294 35587515
    [Google Scholar]
  27. Xiong Y. The emerging role of PANoptosis in cancer treat-ment. Biomed. Pharmacother. 2023 168 115696 10.1016/j.biopha.2023.115696 37837884
    [Google Scholar]
  28. Wei S. Chen Z. Ling X. Zhang W. Jiang L. Comprehen-sive analysis illustrating the role of PANoptosis-related genes in lung cancer based on bioinformatic algorithms and experi-ments. Front. Pharmacol. 2023 14 1115221 10.3389/fphar.2023.1115221 36874021
    [Google Scholar]
  29. Yatim N. Jusforgues-Saklani H. Orozco S. Schulz O. Barreira da Silva R. Reis e Sousa C. Green D.R. Oberst A. Albert M.L. RIPK1 and NF-κB signaling in dying cells de-termines cross-priming of CD8 + T cells. Science 2015 350 6258 328 334 10.1126/science.aad0395 26405229
    [Google Scholar]
  30. Hodge G. Barnawi J. Jurisevic C. Moffat D. Holmes M. Reynolds P.N. Jersmann H. Hodge S. Lung cancer is asso-ciated with decreased expression of perforin, granzyme B and interferon (IFN)-γ by infiltrating lung tissue T cells, natural killer (NK) T-like and NK cells. Clin. Exp. Immunol. 2014 178 1 79 85 10.1111/cei.12392 24894428
    [Google Scholar]
  31. Nwosu Z.C. Ebert M.P. Dooley S. Meyer C. Caveolin-1 in the regulation of cell metabolism: a cancer perspective. Mol. Cancer 2016 15 1 71 10.1186/s12943‑016‑0558‑7 27852311
    [Google Scholar]
  32. Burgermeister E. Liscovitch M. Röcken C. Schmid R.M. Ebert M.P.A. Caveats of caveolin-1 in cancer progression. Cancer Lett. 2008 268 2 187 201 10.1016/j.canlet.2008.03.055 18482795
    [Google Scholar]
  33. Gupta R. Toufaily C. Annabi B. Caveolin and cavin family members: dual roles in cancer. Biochimie 2014 107 188 202 10.1016/j.biochi.2014.09.010
    [Google Scholar]
  34. Wang Z. Wang N. Liu P. Peng F. Tang H. Chen Q. Xu R. Dai Y. Lin Y. Xie X. Peng C. Situ H. Caveolin-1, a stress-related oncotarget, in drug resistance. Oncotarget 2015 6 35 37135 37150 10.18632/oncotarget.5789 26431273
    [Google Scholar]
  35. Zhan P. Shen X.K. Qian Q. Wang Q. Zhu J.P. Zhang Y. Xie H.Y. Xu C.H. Hao K.K. Hu W. Xia N. Lu G.J. Yu L.K. Expression of caveolin-1 is correlated with disease stage and survival in lung adenocarcinomas. Oncol. Rep. 2012 27 4 1072 1078 10.3892/or.2011.1605 22200856
    [Google Scholar]
  36. Grünwald B. Schoeps B. Krüger A. Recognizing the Mo-lecular Multifunctionality and Interactome of TIMP-1. Trends Cell Biol. 2019 29 1 6 19 10.1016/j.tcb.2018.08.006 30243515
    [Google Scholar]
  37. Jackson H.W. Defamie V. Waterhouse P. Khokha R. TIMPs: versatile extracellular regulators in cancer. Nat. Rev. Cancer 2017 17 1 38 53 10.1038/nrc.2016.115 27932800
    [Google Scholar]
  38. Chang Y.H. Chiu Y.J. Cheng H.C. Liu F.J. Lai W.W. Chang H.J. Liao P.C. Down-regulation of TIMP-1 inhibits cell migration, invasion, and metastatic colonization in lung adenocarcinoma. Tumour Biol. 2015 36 5 3957 3967 10.1007/s13277‑015‑3039‑5 25578494
    [Google Scholar]
  39. Maury E. Brichard S.M. Pataky Z. Carpentier A. Golay A. Bobbioni-Harsch E. Effect of obesity on growth-related oncogene factor-alpha, thrombopoietin, and tissue inhibitor metalloproteinase-1 serum levels. Obesity (Silver Spring) 2010 18 8 1503 1509 10.1038/oby.2009.464 20035279
    [Google Scholar]
  40. Papazoglou D. Papatheodorou K. Papanas N. Papadopou-los T. Gioka T. Kabouromiti G. Kotsiou S. Maltezos E. Matrix metalloproteinase-1 and tissue inhibitor of metallopro-teinases-1 levels in severely obese patients: what is the effect of weight loss? Exp. Clin. Endocrinol. Diabetes 2010 118 10 730 734 10.1055/s‑0030‑1249671 20361393
    [Google Scholar]
  41. Dantas E. Murthy A. Ahmed T. Ahmed M. Ramsamooj S. Hurd M.A. Lam T. Malbari M. Agrusa C. Elemento O. Zhang C. Pappin D.J. McGraw T.E. Stiles B.M. Al-torki N.K. Goncalves M.D. TIMP1 is an early biomarker for detection and prognosis of lung cancer. Clin. Transl. Med. 2023 13 10 e1391 10.1002/ctm2.1391 37759102
    [Google Scholar]
  42. Krist A.H. Davidson K.W. Mangione C.M. Barry M.J. Cabana M. Caughey A.B. Davis E.M. Donahue K.E. Doubeni C.A. Kubik M. Landefeld C.S. Li L. Ogedegbe G. Owens D.K. Pbert L. Silverstein M. Stevermer J. Tseng C.W. Wong J.B. Screening for lung cancer. JAMA 2021 325 10 962 970 10.1001/jama.2021.1117 33687470
    [Google Scholar]
  43. Zhou X. Zhang P. Luo W. Zhang L. Hu R. Sun Y. Jiang H. Ketamine induces apoptosis in lung adenocarcinoma cells by regulating the expression of CD 69. Cancer Med. 2018 7 3 788 795 10.1002/cam4.1288 29453833
    [Google Scholar]
  44. Pajusto M. Ihalainen N. Pelkonen J. Tarkkanen J. Mat-tila P.S. Human in vivo ‐activated CD45R0 + CD4 + T cells are susceptible to spontaneous apoptosis that can be inhibited by the chemokine CXCL12 and IL‐2, ‐6, ‐7, and ‐15. Eur. J. Immunol. 2004 34 10 2771 2780 10.1002/eji.200324761 15368293
    [Google Scholar]
  45. Foerster M. Haefner D. Kroegel C. Bcl-2-mediated regula-tion of CD69-induced apoptosis of human eosinophils: iden-tification and characterization of a novel receptor-induced mechanism and relationship to CD95-transduced signalling. Scand. J. Immunol. 2002 56 4 417 428 10.1046/j.1365‑3083.2002.01111.x 12234263
    [Google Scholar]
  46. Gorabi A.M. Hajighasemi S. Kiaie N. Gheibi Hayat S.M. Jamialahmadi T. Johnston T.P. Sahebkar A. The pivotal role of CD69 in autoimmunity. J. Autoimmun. 2020 111 102453 10.1016/j.jaut.2020.102453 32291138
    [Google Scholar]
  47. Hu Z.W. Sun W. Wen Y.H. Ma R.Q. Chen L. Chen W.Q. Lei W.B. Wen W.P. CD69 and SBK1 as potential pre-dictors of responses to PD-1/PD-L1 blockade cancer immu-notherapy in lung cancer and melanoma. Front. Immunol. 2022 13 952059 10.3389/fimmu.2022.952059 36045683
    [Google Scholar]
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Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study
Bentham Science Publishers ; https://doi.org/10.2174/0115680096322045240902103219
/content/journals/ccdt/10.2174/0115680096322045240902103219
/content/journals/ccdt/10.2174/0115680096322045240902103219
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  • Article Type:     Research Article
Keywords: machine learning ; PANoptosis ; Lung adenocarcinoma ; bioinformatics

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