Skip to content
2000
image of A Lactylation-based Gene Signature in Lung Adenocarcinoma Provides a Novel Perspective to Predict the Prognosis and Therapeutic Response

Abstract

Introduction

Lactylation, a novel post-translational modification in tumor cells, is studied here to explore its relevant gene signature in lung adenocarcinoma (LUAD).

Materials and Methods

Transcriptome data of LUAD from The Cancer Genome Atlas (TCGA) were retrieved from UCSC Xena, while the gene expression dataset GSE31210 was acquired from the Gene Expression Omnibus (GEO) database. Lactation-related genes (LRGs) were identified based on previous literature. Lactation levels were quantified using single-sample gene set enrichment analysis (ssGSEA). After screening LRGs associated with prognosis, the ConsensusClusterPlus package was employed for clustering. Subsequently, a RiskScore model was constructed through univariate Cox and LASSO analyses. The prognostic relevance and robustness of the RiskScore were evaluated, and the association of the RiskScore with both immunotherapy response and the tumor microenvironment (TME) was determined.

Results

An overall high lactylation score was noticed in the LUAD sample, which could be assigned into 2 molecular subtypes (C1 and C2). Meanwhile, 83 LRGs were preliminarily explored to have prognostic relevance, of which 5 genes (, , , , and ) were further revealed as the hub genes for the RiskScore model. Such RiskScore, independent of other clinicopathological features, displayed a satisfying efficacy in predicting the prognosis and the response to immunotherapy. Additionally, the formulated RiskScore, including the expression level of each gene, displayed a varied correlation with the predicted drug candidates.

Discussion

From the lactylation perspective, this study reveals LUAD’s molecular heterogeneity, proposes and externally validates a 5-gene RiskScore model. The model independently predicts prognosis, correlates with immunotherapeutic response and drug sensitivity, and points to potential links among lactate metabolism, key signaling pathways, and clinical outcomes.

Conclusion

5 prognosis-relevant LRGs could provide a novel perspective for the individualized therapeutic regimens for LUAD.

Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673433855251202003730
2026-03-06
2026-03-10
Loading full text...

Full text loading...

References

  1. Thai A.A. Solomon B.J. Sequist L.V. Gainor J.F. Heist R.S. Lung cancer. Lancet 2021 398 10299 535 554 10.1016/S0140‑6736(21)00312‑3 34273294
    [Google Scholar]
  2. Ding Y. Lv J. Hua Y. Comprehensive metabolomic analysis of lung cancer patients treated with Fu Zheng Fang. Curr. Pharm. Anal. 2022 18 9 881 891 10.2174/1573412918666220822143119
    [Google Scholar]
  3. Li Y. Yan B. He S. Advances and challenges in the treatment of lung cancer. Biomed Pharmacother 2023 169 115891 10.1016/j.biopha.2023.115891 37979378
    [Google Scholar]
  4. Feng X. Zhu B. Peng Y. Zhang K. Wang Y. Huang G. Li Y. Functions of CAFs in microenvironment of non-small cell lung cancer: Based on updated hallmarks of cancer. Oncologie 2024 26 5 701 709 10.1515/oncologie‑2024‑0232
    [Google Scholar]
  5. Qian X.J. Wang J.W. Liu J.B. Yu X. The mediating role of miR-451/ETV4/MMP13 signaling axis on epithelialmesenchymal transition in promoting non-small cell lung cancer progression. Curr. Mol. Pharmacol. 2023 17 e210723218988 10.2174/1874467217666230721123554 37489792
    [Google Scholar]
  6. Hirsch F.R. Scagliotti G.V. Mulshine J.L. Kwon R. Curran W.J. Jr Wu Y.L. Paz-Ares L. Lung cancer: Current therapies and new targeted treatments. Lancet 2017 389 10066 299 311 10.1016/S0140‑6736(16)30958‑8 27574741
    [Google Scholar]
  7. Kris M.G. Gaspar L.E. Chaft J.E. Kennedy E.B. Azzoli C.G. Ellis P.M. Lin S.H. Pass H.I. Seth R. Shepherd F.A. Spigel D.R. Strawn J.R. Ung Y.C. Weyant M. Adjuvant systemic therapy and adjuvant radiation therapy for stage I to IIIA completely resected non–small-cell lung cancers: American society of clinical oncology/cancer care ontario clinical practice guideline update. J. Clin. Oncol. 2017 35 25 2960 2974 10.1200/JCO.2017.72.4401 28437162
    [Google Scholar]
  8. Liu S.Y. Sun H. Zhou J.Y. Jie G.L. Xie Z. Shao Y. Zhang X. Ye J.Y. Chen C.X. Zhang X.C. Zhou Q. Yang J.J. Wu Y.L. Clinical characteristics and prognostic value of the KRAS G12C mutation in Chinese non-small cell lung cancer patients. Biomark. Res. 2020 8 1 22 10.1186/s40364‑020‑00199‑z 32607238
    [Google Scholar]
  9. Huang L. Guo Z. Wang F. Fu L. KRAS mutation: From undruggable to druggable in cancer. Signal Transduct. Target. Ther. 2021 6 1 386 10.1038/s41392‑021‑00780‑4 34776511
    [Google Scholar]
  10. Wei X. Li X. Hu S. Cheng J. Cai R. Regulation of ferroptosis in lung adenocarcinoma. Int. J. Mol. Sci. 2023 24 19 14614 10.3390/ijms241914614 37834062
    [Google Scholar]
  11. Chen J. Zhu Y. Wu C. Shi J. Engineering lactate-modulating nanomedicines for cancer therapy. Chem. Soc. Rev. 2023 52 3 973 1000 10.1039/D2CS00479H 36597879
    [Google Scholar]
  12. Hui S. Ghergurovich J.M. Morscher R.J. Jang C. Teng X. Lu W. Esparza L.A. Reya T. Le Zhan Yanxiang Guo J. White E. Rabinowitz J.D. Glucose feeds the TCA cycle via circulating lactate. Nature 2017 551 7678 115 118 10.1038/nature24057 29045397
    [Google Scholar]
  13. Peng T. Sun F. Yang J.C. Cai M.H. Huai M.X. Pan J.X. Zhang F.Y. Xu L.M. Novel lactylation-related signature to predict prognosis for pancreatic adenocarcinoma. World J. Gastroenterol. 2024 30 19 2575 2602 10.3748/wjg.v30.i19.2575 38817665
    [Google Scholar]
  14. Certo M. Tsai C.H. Pucino V. Ho P.C. Mauro C. Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat. Rev. Immunol. 2021 21 3 151 161 10.1038/s41577‑020‑0406‑2 32839570
    [Google Scholar]
  15. Zhang D. Tang Z. Huang H. Zhou G. Cui C. Weng Y. Liu W. Kim S. Lee S. Perez-Neut M. Ding J. Czyz D. Hu R. Ye Z. He M. Zheng Y.G. Shuman H.A. Dai L. Ren B. Roeder R.G. Becker L. Zhao Y. Metabolic regulation of gene expression by histone lactylation. Nature 2019 574 7779 575 580 10.1038/s41586‑019‑1678‑1 31645732
    [Google Scholar]
  16. Xiong J. He J. Zhu J. Pan J. Liao W. Ye H. Wang H. Song Y. Du Y. Cui B. Xue M. Zheng W. Kong X. Jiang K. Ding K. Lai L. Wang Q. Lactylation-driven METTL3-mediated RNA m6 A modification promotes immunosuppression of tumor-infiltrating myeloid cells. Mol Cell 2022 82 9 1660 1677.e10 10.1016/j.molcel.2022.02.033 35320754
    [Google Scholar]
  17. Yu J. Chai P. Xie M. Ge S. Ruan J. Fan X. Jia R. Histone lactylation drives oncogenesis by facilitating m6 A reader protein YTHDF2 expression in ocular melanoma. Genome Biol. 2021 22 1 85 10.1186/s13059‑021‑02308‑z 33726814
    [Google Scholar]
  18. Xie B. Lin J. Chen X. Zhou X. Zhang Y. Fan M. Xiang J. He N. Hu Z. Wang F. CircXRN2 suppresses tumor progression driven by histone lactylation through activating the Hippo pathway in human bladder cancer. Mol. Cancer 2023 22 1 151 10.1186/s12943‑023‑01856‑1 37684641
    [Google Scholar]
  19. Yang K. Fan M. Wang X. Xu J. Wang Y. Tu F. Gill P.S. Ha T. Liu L. Williams D.L. Li C. Lactate promotes macrophage HMGB1 lactylation, acetylation, and exosomal release in polymicrobial sepsis. Cell Death Differ. 2022 29 1 133 146 10.1038/s41418‑021‑00841‑9 34363018
    [Google Scholar]
  20. de Sousa V.M.L. Carvalho L. Heterogeneity in lung cancer. Pathobiology 2018 85 1-2 96 107 10.1159/000487440 29635240
    [Google Scholar]
  21. Wang C. Yu Q. Song T. Wang Z. Song L. Yang Y. Shao J. Li J. Ni Y. Chao N. Zhang L. Li W. The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing. Signal Transduct. Target. Ther. 2022 7 1 289 10.1038/s41392‑022‑01130‑8 36008393
    [Google Scholar]
  22. Kukurba K.R. Montgomery S.B. RNA sequencing and analysis. Cold Spring Harb. Protoc. 2015 2015 11 pdb.top084970 10.1101/pdb.top084970 25870306
    [Google Scholar]
  23. Jiang T. Zheng J. Li N. Li X. He J. Zhou J. Sun B. Chi Q. Dissecting the mechanisms of intestinal immune homeostasis by analyzing T-cell immune response in Crohn’s disease and colorectal cancer. Curr. Gene Ther. 2024 24 5 422 440 10.2174/0115665232294568240201073417 38682449
    [Google Scholar]
  24. Huang H. Chen K. Zhu Y. Hu Z. Wang Y. Chen J. Li Y. Li D. Wei P. A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer. J. Transl. Med. 2024 22 1 211 10.1186/s12967‑024‑04955‑9 38419085
    [Google Scholar]
  25. Wilkerson M.D. Hayes D.N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics 2010 26 12 1572 1573 10.1093/bioinformatics/btq170 20427518
    [Google Scholar]
  26. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  27. Song Z. Yu J. Wang M. Shen W. Wang C. Lu T. Shan G. Dong G. Wang Y. Zhao J. CHDTEPDB: Transcriptome expression profile database and interactive analysis platform for congenital heart disease. Congenit. Heart Dis. 2023 18 6 693 701 10.32604/chd.2024.048081
    [Google Scholar]
  28. Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012 16 5 284 287 10.1089/omi.2011.0118 22455463
    [Google Scholar]
  29. Engebretsen S. Bohlin J. Statistical predictions with glmnet. Clin. Epigenetics 2019 11 1 123 10.1186/s13148‑019‑0730‑1 31443682
    [Google Scholar]
  30. Li M. Wei X. Zhang S.S. Li S. Chen S.H. Shi S.J. Zhou S.H. Sun D.Q. Zhao Q.Y. Xu Y. Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: Development and validation of a predictive nomogram model. BMC Pulm. Med. 2023 23 1 383 10.1186/s12890‑023‑02684‑1 37817172
    [Google Scholar]
  31. Li X. Lei J. Shi Y. Peng Z. Gong M. Shu X. Developing a riskscore model based on angiogenesis-related lncRNAs for colon adenocarcinoma prognostic prediction. Curr. Med. Chem. 2024 31 17 2449 2466 10.2174/0109298673277243231108071620 37961859
    [Google Scholar]
  32. Charoentong P. Finotello F. Angelova M. Mayer C. Efremova M. Rieder D. Hackl H. Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017 18 1 248 262 10.1016/j.celrep.2016.12.019 28052254
    [Google Scholar]
  33. 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]
  34. Liberti M.V. Locasale J.W. The warburg effect: How Does it benefit cancer cells? Trends Biochem. Sci. 2016 41 3 211 218 10.1016/j.tibs.2015.12.001 26778478
    [Google Scholar]
  35. Pucino V. Cucchi D. Mauro C. Lactate transporters as therapeutic targets in cancer and inflammatory diseases. Expert Opin. Ther. Targets 2018 22 9 735 743 10.1080/14728222.2018.1511706 30106309
    [Google Scholar]
  36. Böttcher M. Baur R. Stoll A. Mackensen A. Mougiakakos D. Linking immunoevasion and metabolic reprogramming in B-cell–derived lymphomas. Front. Oncol. 2020 10 594782 10.3389/fonc.2020.594782 33251150
    [Google Scholar]
  37. Chen L. Huang L. Gu Y. Cang W. Sun P. Xiang Y. Lactate-lactylation hands between metabolic reprogramming and immunosuppression. Int. J. Mol. Sci. 2022 23 19 11943 10.3390/ijms231911943 36233246
    [Google Scholar]
  38. Cheng Z. Huang H. Li M. Liang X. Tan Y. Chen Y. Lactylation-related gene signature effectively predicts prognosis and treatment responsiveness in hepatocellular carcinoma. Pharmaceuticals 2023 16 5 644 10.3390/ph16050644 37242427
    [Google Scholar]
  39. Gao M. Wang M. Zhou S. Hou J. He W. Shu Y. Wang X. Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma. Cancer Cell Int. 2024 24 1 400 10.1186/s12935‑024‑03592‑y 39696439
    [Google Scholar]
  40. Yang Y.H. Wang Q.C. Kong J. Yang J.T. Liu J.F. Global profiling of lysine lactylation in human lungs. Proteomics 2023 23 15 2200437 10.1002/pmic.202200437 37170646
    [Google Scholar]
  41. Wang K. Zhang M. Wang J. Sun P. Luo J. Jin H. Li R. Pan C. Lu L. A systematic analysis identifies key regulators involved in cell proliferation and potential drugs for the treatment of human lung adenocarcinoma. Front. Oncol. 2021 11 737152 10.3389/fonc.2021.737152 34650921
    [Google Scholar]
  42. Weitzer S. Uhlmann F. Chromosome segregation. Dev. Cell 2002 2 4 381 382 10.1016/S1534‑5807(02)00155‑7 11970886
    [Google Scholar]
  43. Hanahan D. Weinberg R.A. Hallmarks of cancer: The next generation. Cell 2011 144 5 646 674 10.1016/j.cell.2011.02.013 21376230
    [Google Scholar]
  44. Bolhaqueiro A.C.F. Ponsioen B. Bakker B. Klaasen S.J. Kucukkose E. van Jaarsveld R.H. Vivié J. Verlaan-Klink I. Hami N. Spierings D.C.J. Sasaki N. Dutta D. Boj S.F. Vries R.G.J. Lansdorp P.M. van de Wetering M. van Oudenaarden A. Clevers H. Kranenburg O. Foijer F. Snippert H.J.G. Kops G.J.P.L. Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids. Nat. Genet. 2019 51 5 824 834 10.1038/s41588‑019‑0399‑6 31036964
    [Google Scholar]
  45. Tayoun T. Oulhen M. Aberlenc A. Farace F. Pawlikowska P. Tumor evolution and therapeutic choice seen through a prism of circulating tumor cell genomic instability. Cells 2021 10 2 337 10.3390/cells10020337 33562741
    [Google Scholar]
  46. Huang Y. He J. Duan X. Hou R. Shi J. Prognostic gene HLA‐DMA associated with cell cycle and immune infiltrates in LUAD. Clin. Respir. J. 2023 17 12 1286 1300 10.1111/crj.13716 37972401
    [Google Scholar]
  47. Li Z. Zheng Y. Wu Z. Zhuo T. Zhu Y. Dai L. Wang Y. Chen M. NCAPD2 is a novel marker for the poor prognosis of lung adenocarcinoma and is associated with immune infiltration and tumor mutational burden. Medicine 2023 102 3 e32686 10.1097/MD.0000000000032686 36701707
    [Google Scholar]
  48. Zhang C. Shen Q. Gao M. Li J. Pang B. The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis. Heliyon 2024 10 4 e26061 10.1016/j.heliyon.2024.e26061 38380029
    [Google Scholar]
  49. Li Z. Shi J. Zhang N. Zheng X. Jin Y. Wen S. Hu W. Wu Y. Gao W. FSCN1 acts as a promising therapeutic target in the blockade of tumor cell motility: A review of its function, mechanism, and clinical significance. J. Cancer 2022 13 8 2528 2539 10.7150/jca.67977 35711849
    [Google Scholar]
  50. Cheng C. Pei X. Li S.W. Yang J. Li C. Tang J. Hu K. Huang G. Min W.P. Sang Y. CRISPR/Cas9 library screening uncovered methylated PKP2 as a critical driver of lung cancer radioresistance by stabilizing β-catenin. Oncogene 2021 40 16 2842 2857 10.1038/s41388‑021‑01692‑x 33742119
    [Google Scholar]
  51. Zheng Y.W. Li Z.H. Lei L. Liu C.C. Wang Z. Fei L.R. Yang M.Q. Huang W.J. Xu H.T. FAM83A promotes lung cancer progression by regulating the Wnt and hippo signaling pathways and indicates poor prognosis. Front. Oncol. 2020 10 180 10.3389/fonc.2020.00180 32195172
    [Google Scholar]
  52. Chen Y. Zhou H. Yang S. Su D. Increased ABCC2 expression predicts cisplatin resistance in non-small cell lung cancer. Cell Biochem. Funct. 2021 39 2 277 286 10.1002/cbf.3577 32815556
    [Google Scholar]
  53. Yanli Luo Sheng Zhang Huilin Xie Qiaofeng Su Shuang He Zhen Lei. Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in lung adenocarcinoma. Cell. Mol. Biol. 2023 69 14 51 61 10.14715/cmb/2023.69.14.9 38279482
    [Google Scholar]
  54. Cong D. Zhao Y. Zhang W. Li J. Bai Y. Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism. Front. Pharmacol. 2023 14 1260742 10.3389/fphar.2023.1260742 37920207
    [Google Scholar]
  55. Sun J. Jiang R. Hou L. Wang L. Li M. Dong H. Dong N. Lin Y. Zhu Z. Zhang G. Zhang Y. Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma. BMC Pulm. Med. 2024 24 1 323 10.1186/s12890‑024‑03132‑4 38965505
    [Google Scholar]
  56. Luan Y. Liang C. Han Q. Zhou X. Yang N. Zhao The systematic analysis of genes related to butyrate metabolism suggests that CDKN3 could serve as a promising therapeutic target for lung adenocarcinoma treatment. BMC Cancer 2025 25 1 69 10.1186/s12885‑024‑13409‑w 39806313
    [Google Scholar]
  57. Sarvaria A. Madrigal J.A. Saudemont A. B cell regulation in cancer and anti-tumor immunity. Cell. Mol. Immunol. 2017 14 8 662 674 10.1038/cmi.2017.35 28626234
    [Google Scholar]
  58. Schwartz M. Zhang Y. Rosenblatt J.D. B cell regulation of the anti-tumor response and role in carcinogenesis. J. Immunother. Cancer 2016 4 1 40 10.1186/s40425‑016‑0145‑x 27437104
    [Google Scholar]
  59. Hanley C.J. Waise S. Ellis M.J. Lopez M.A. Pun W.Y. Taylor J. Parker R. Kimbley L.M. Chee S.J. Shaw E.C. West J. Alzetani A. Woo E. Ottensmeier C.H. Rose-Zerilli M.J.J. Thomas G.J. Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer. Nat. Commun. 2023 14 1 387 10.1038/s41467‑023‑35832‑6 36720863
    [Google Scholar]
  60. Borst J. Ahrends T. Bąbała N. Melief C.J.M. Kastenmüller W. CD4+ T cell help in cancer immunology and immunotherapy. Nat. Rev. Immunol. 2018 18 10 635 647 10.1038/s41577‑018‑0044‑0 30057419
    [Google Scholar]
  61. Saura-Esteller J. de Jong M. King L.A. Ensing E. Winograd B. de Gruijl T.D. Parren P.W.H.I. van der Vliet H.J. Gamma delta T-cell based cancer immunotherapy: Past-present-future. Front. Immunol. 2022 13 915837 10.3389/fimmu.2022.915837 35784326
    [Google Scholar]
  62. Diao Y. Ma X. Min W. Lin S. Kang H. Dai Z. Wang X. Zhao Y. Dasatinib promotes paclitaxel-induced necroptosis in lung adenocarcinoma with phosphorylated caspase-8 by c-Src. Cancer Lett. 2016 379 1 12 23 10.1016/j.canlet.2016.05.003 27195913
    [Google Scholar]
  63. Reck M. Kaiser R. Mellemgaard A. Douillard J.Y. Orlov S. Krzakowski M. von Pawel J. Gottfried M. Bondarenko I. Liao M. Gann C.N. Barrueco J. Gaschler-Markefski B. Novello S. Docetaxel plus nintedanib versus docetaxel plus placebo in patients with previously treated non-small-cell lung cancer (LUME-Lung 1): A phase 3, double-blind, randomised controlled trial. Lancet Oncol. 2014 15 2 143 155 10.1016/S1470‑2045(13)70586‑2 24411639
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673433855251202003730
Loading
/content/journals/cmc/10.2174/0109298673433855251202003730
Loading

Data & Media loading...

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