Skip to content
2000
Volume 21, Issue 9
  • ISSN: 1573-4110
  • E-ISSN: 1875-6727

Abstract

Introduction

Lung adenocarcinoma (LUAD) exhibits high incidence and mortality rates globally. Mitophagy exerts a critical role in cancer development, including LUAD. The present work set out to classify the molecular subtypes of LUAD and to develop a mitophagy-related gene (MRG) signature to assess the prognostic outcomes of LUAD patients.

Methods

Two datasets were acquired from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. MRGs were extracted from the MSigDB. The somatic gene mutation landscape was developed using the “maftools” package. Molecular subtypes were classified by employing the “ConsensusClusterPlus” package. Functional enrichment analysis was performed using the “clusterProfiler” package. Mitophagy-related module genes were identified using the “WGCNA” package and used to develop a prognostic MRG signature employing LASSO Cox regression analysis. Then, a RiskScore model was formulated and validated. Immune cell infiltration in different groups was compared. The expressions of the prognostic MRGs in LUAD cells were detected by tests. CCK-8, wound healing, and transwell assays were carried out to measure the cell viability, and migratory and invasive capabilities of LUAD cells.

Results

Somatic gene mutation was detected in 77 (13.58%) out of 567 LUAD patients and 10 (50%) out of 20 prognosis-related MRGs. Based on 20 prognosis-related MRGs, three molecular LUAD subtypes with distinct prognostic outcomes, clinical features, immune cell infiltration, and biological pathways were classified. Next, a 9-MRG signature composed of 3 “protective” genes (, , ) and 6 “risk” genes (, , , , , ) was established. Then, a RiskScore model with excellent prognostic predictive power for LUAD was constructed. The high-risk group showed worse outcomes and decreased immune cell infiltration in comparison to the low-risk group. Further, the relative mRNA expressions of , , and were significantly downregulated, while those of , , , , , and were notably upregulated in LUAD cells. In addition, silencing and significantly affected the invasive and migratory capacities of LUAD cells.

Conclusion

We delineated three molecular subtypes and developed a 9-MRG signature in LUAD, providing a valuable framework for the prognosis evaluation of LUAD patients.

Loading

Article metrics loading...

/content/journals/cac/10.2174/0115734110370974250117074604
2025-01-21
2025-12-14
Loading full text...

Full text loading...

References

  1. MengX. ZhaoX. ZhouB. SongW. LiangY. LiangM. DuM. ShiJ. GaoY. FSTL3 is associated with prognosis and immune cell infiltration in lung adenocarcinoma.J. Cancer Res. Clin. Oncol.202415011710.1007/s00432‑023‑05553‑w 38240936
    [Google Scholar]
  2. ChenZ. LinJ. WangH. WangJ. ZhangZ. Expression and clinical role of PRDX6 in lung adenocarcinoma.J. Int. Med. Res.20245230300060524123627610.1177/03000605241236276 38506348
    [Google Scholar]
  3. BosganaP. NikouS. DimitrakopoulosF.I. LogothetiS. TzelepiV. KalophonosC. BravouV. KoureaE. SampsonasF. ZolotaV. H3K4 methylation status and lysine specific methyltransferase KMT2C expression correlate with prognosis in lung adenocarcinoma.Curr. Mol. Pharmacol.20211461028103610.2174/1874467213999200831130739 32867667
    [Google Scholar]
  4. WangJ. CaiY. ShengZ. DongZ. EGFR Inhibitor CL-387785 Suppresses the Progression of Lung Adenocarcinoma.Curr. Mol. Pharmacol.202316221121610.2174/1874467215666220329212300 35352671
    [Google Scholar]
  5. ZhongW. ZhangW. DaiL. ChenM. The clinical, radiological, postoperative pathological, and genetic features of nodular lung adenocarcinoma: A real-world single-center data.J. Thorac. Dis.20241653228325010.21037/jtd‑24‑510 38883620
    [Google Scholar]
  6. ZhangL. MengQ. ZhuangL. GongQ. HuangX. LiX. LiS. WangG. WangX. miR-30a-5p/PHTF2 axis regulates the tumorigenesis and metastasis of lung adenocarcinoma.Biocell202448458159010.32604/biocell.2024.047260
    [Google Scholar]
  7. TianZ. YuS. CaiR. ZhangY. LiuQ. ZhuY. SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: A machine-learning based approach.Biochem. Biophys. Rep.20243810169310.1016/j.bbrep.2024.101693 38571554
    [Google Scholar]
  8. LiY. ZhaoJ. ZhaoY. LiR. DongX. YaoX. XiaZ. XuY. LiY. Survival benefit of adjuvant chemotherapy after resection of Stage I lung adenocarcinoma containing micropapillary components.Cancer Med.2024133e703010.1002/cam4.7030 38400663
    [Google Scholar]
  9. ZhengH. TanJ. QinF. ZhengY. YangX. QinX. LiaoH. Analysis of cancer-associated fibroblasts related genes identifies COL11A1 associated with lung adenocarcinoma prognosis.BMC Med. Genomics20241719710.1186/s12920‑024‑01863‑1 38649961
    [Google Scholar]
  10. XiaX. GeY. GeF. GuP. LiuY. LiP. XuP. MAP4 acts as an oncogene and prognostic marker and affects radioresistance by mediating epithelial–mesenchymal transition in lung adenocarcinoma.J. Cancer Res. Clin. Oncol.202415028810.1007/s00432‑024‑05614‑8 38341398
    [Google Scholar]
  11. LinS. HeC. SongL. SunL. ZhaoR. MinW. ZhaoY. Exosomal lncCRLA is predictive for the evolvement and development of lung adenocarcinoma.Cancer Lett.202458221658810.1016/j.canlet.2023.216588 38097132
    [Google Scholar]
  12. SunZ. SunJ. HuH. HanS. MaP. ZuoB. WangZ. LiuZ. A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma.Heliyon2024109e3064610.1016/j.heliyon.2024.e30646 38765119
    [Google Scholar]
  13. OnishiM. YamanoK. SatoM. MatsudaN. OkamotoK. Molecular mechanisms and physiological functions of mitophagy.EMBO J.2021403e10470510.15252/embj.2020104705 33438778
    [Google Scholar]
  14. WengJ.S. HuangJ.P. YuW. XiaoJ. LinF. LinK.N. ZangW.D. YeY. LinJ.P. Mitophagy-related gene signature predicts prognosis, immune infiltration and chemotherapy sensitivity in colorectal cancer.World J. Gastrointest. Oncol.202315354656110.4251/wjgo.v15.i3.546 37009318
    [Google Scholar]
  15. ZhuoZ. LinH. LiangJ. MaP. LiJ. HuangL. ChenL. YangH. BaiY. ShaW. Mitophagy-related gene signature for prediction prognosis, immune scenery, mutation, and chemotherapy response in pancreatic cancer.Front. Cell Dev. Biol.2022980252810.3389/fcell.2021.802528 35198564
    [Google Scholar]
  16. WangJ. QiuX. HuangJ. ZhuoZ. ChenH. ZengR. WuH. GuoK. YangQ. YeH. HuangW. LuoY. Development and validation of a novel mitophagy-related gene prognostic signature for glioblastoma multiforme.BMC Cancer202222164410.1186/s12885‑022‑09707‑w 35692054
    [Google Scholar]
  17. FengZ. YuanL. MaL. YuW. KheirF. KäsmannL. BruecklW.M. JinK. WangD. ShenY. LiR. TianH. Peptidyl-prolyl isomerase F as a prognostic biomarker associated with immune infiltrates and mitophagy in lung adenocarcinoma.Transl. Lung Cancer Res.20241361346136410.21037/tlcr‑24‑344 38973949
    [Google Scholar]
  18. LiL. HuF. Mitophagy in tumor: Foe or friend?Endokrynol. Pol.202374551151910.5603/ep.95652 37902014
    [Google Scholar]
  19. LvT. ZhangH. Mitophagy-related gene signature for predicting the prognosis of multiple myeloma.Heliyon2024103e2452010.1016/j.heliyon.2024.e24520 38317923
    [Google Scholar]
  20. LiuJ. HuangH. HanY. HuaY. LiB. LiuH. ChenJ. Genomic analysis of hypoxia and mitophagy related genes with prognosis and characterization of the immune microenvironment in LUAD.J. Cancer20241551342135410.7150/jca.91762 38356715
    [Google Scholar]
  21. JiJ. WangK. MengX. ZhongH. LiX. ZhaoH. XieG. XieY. WangX. ZhuX. Elaiophylin inhibits tumorigenesis of human lung adenocarcinoma by inhibiting mitophagy via suppression of SIRT1/Nrf2 signaling.Cancers (Basel)20221423581210.3390/cancers14235812 36497294
    [Google Scholar]
  22. ZhuG. PeiL. LiY. GouX. EP300 mutation is associated with tumor mutation burden and promotes antitumor immunity in bladder cancer patients.Aging (Albany NY)20201232132214110.18632/aging.102728 32012118
    [Google Scholar]
  23. SongZ. CHDTEPDB: Transcriptome expression profile database and interactive analysis platform for congenital heart disease.Congenit. Heart Dis.202318669370110.32604/chd.2024.048081
    [Google Scholar]
  24. YuH. LiuQ. JinM. HuangG. CaiQ. Comprehensive analysis of mitophagy-related genes in NSCLC diagnosis and immune scenery: Based on bulk and single-cell RNA sequencing data.Front. Immunol.202314127607410.3389/fimmu.2023.1276074 38155968
    [Google Scholar]
  25. PatiyalS. DhallA. RaghavaG.P.S. Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques.Biol. Methods Protoc.202271bpac01210.1093/biomethods/bpac012 35734767
    [Google Scholar]
  26. MayakondaA. LinD.C. AssenovY. PlassC. KoefflerH.P. Maftools: Efficient and comprehensive analysis of somatic variants in cancer.Genome Res.201828111747175610.1101/gr.239244.118 30341162
    [Google Scholar]
  27. ZhengS. WangX. FuY. LiB. XuJ. WangH. HuangZ. XuH. QiuY. ShiY. LiK. Targeted next-generation sequencing for cancer-associated gene mutation and copy number detection in 206 patients with non–small-cell lung cancer.Bioengineered202112179180210.1080/21655979.2021.1890382 33629637
    [Google Scholar]
  28. JiaS. ZhaiL. WuF. LvW. MinX. ZhangS. LiF. Integrative machine learning algorithms for developing a consensus RNA modification-based signature for guiding clinical decision-making in bladder cancer.Oncologie202426226928510.1515/oncologie‑2023‑0348
    [Google Scholar]
  29. WuZ. LiuZ. GuC. WuY. LiY. ZhouZ. YangX. A multi-cancer analysis unveils ITGBL1 as a cancer prognostic molecule and a novel immunotherapy target.Oncologie202426219521010.1515/oncologie‑2023‑0455
    [Google Scholar]
  30. TsvetkovP. CoyS. PetrovaB. DreishpoonM. VermaA. AbdusamadM. RossenJ. Joesch-CohenL. HumeidiR. SpanglerR.D. EatonJ.K. FrenkelE. KocakM. CorselloS.M. LutsenkoS. KanarekN. SantagataS. GolubT.R. Copper induces cell death by targeting lipoylated TCA cycle proteins.Science202237565861254126110.1126/science.abf0529 35298263
    [Google Scholar]
  31. ZhangY. LiangX. ZhangL. WangD. Metabolic characterization and metabolism-score of tumor to predict the prognosis in prostate cancer.Sci. Rep.20211112248610.1038/s41598‑021‑01140‑6 34795309
    [Google Scholar]
  32. ZhangC. LuoG. LinJ. ZhaoZ. LuoM. LiH. Identification of significant modules and hub genes involved in hepatic encephalopathy using WGCNA.Eur. J. Med. Res.202227126410.1186/s40001‑022‑00898‑3 36424620
    [Google Scholar]
  33. PengS. MaS. YangF. XuC. LiH. LuS. ZhangJ. JiaoJ. HanD. ShiC. ZhangR. YangA.G. ZhangK. WenW. QinW. Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer.Sci. Rep.2022121221910.1038/s41598‑022‑06334‑0 35140317
    [Google Scholar]
  34. PeiY. WuY. ZhangM. SuX. CaoH. ZhaoJ. Identification and analysis of immune microenvironment-related genes for keloid risk prediction and their effects on keloid proliferation and migration.Biochem. Genet.20246243174319710.1007/s10528‑023‑10598‑0 38085498
    [Google Scholar]
  35. WangR. ZhangX. HeC. GuoW. An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases.Front. Genet.202314115632210.3389/fgene.2023.1156322 37124617
    [Google Scholar]
  36. GaoY. ZhangH. TianX. Integrated analysis of TCGA data identifies endoplasmic reticulum stress-related lncRNA signature in stomach adenocarcinoma.Oncologie202426222123710.1515/oncologie‑2023‑0394
    [Google Scholar]
  37. ChuY. YaoY. HuQ. SongQ. Riskscore model based on oxidative stress–related genes may facilitate the prognosis evaluation for patients with colon cancer.Clin. Transl. Gastroenterol.2023146e0058210.14309/ctg.0000000000000582 36927989
    [Google Scholar]
  38. MoB. ZhaoX. WangY. JiangX. LiuD. CaiH. Pan-cancer analysis, providing a reliable basis for IDO2 as a prognostic biomarker and target for immunotherapy.Oncologie2023251173510.1515/oncologie‑2022‑1026
    [Google Scholar]
  39. ZhangH. HuangY. YangE. GaoX. ZouP. SunJ. TianZ. BaoM. LiaoD. GeJ. YangQ. LiX. ZhangZ. LuoP. JiangX. Identification of a fibroblast-related prognostic model in glioma based on bioinformatics methods.Biomolecules20221211159810.3390/biom12111598 36358948
    [Google Scholar]
  40. ZhangX. WangL. YangT. KongL. WeiL. DuJ. Bioinformatic analysis of the role of immune checkpoint genes and immune infiltration in the pathogenesis and development of premature ovarian insufficiency.J. Assist. Reprod. Genet.20244161619163510.1007/s10815‑024‑03120‑x 38695984
    [Google Scholar]
  41. YangC. HuangT. LiangY. XueY. LiangY. WeiX. MengF. WeiQ. CTHRC1 targeted by miR-30a-5p regulates cell adhesion, invasion and migration in lung adenocarcinoma.J. Cardiothorac. Surg.20221714610.1186/s13019‑022‑01788‑9 35313900
    [Google Scholar]
  42. LiM.X. LiZ. ZhangR. YuY. WangL.S. WangQ. DingZ. ZhangJ.P. ZhangM.R. XuL.C. Effects of small interfering RNA-mediated silencing of susceptibility genes of non-syndromic cleft lip with or without cleft palate on cell proliferation and migration.Int. J. Pediatr. Otorhinolaryngol.202013811038210.1016/j.ijporl.2020.110382 33152973
    [Google Scholar]
  43. ChengK. ShiL. ShiC. XieS. WangC. PKHD1L1 blocks the malignant behavior of lung adenocarcinoma cells and restricts tumor growth by regulating CBX7.Biocell20244881209122110.32604/biocell.2024.049626
    [Google Scholar]
  44. LiZ. JiangD. YangS. MiR-490-3p inhibits the malignant progression of lung adenocarcinoma.Cancer Manag. Res.202012109751098410.2147/CMAR.S258182 33154676
    [Google Scholar]
  45. ZhangM. LanX. ChenY. MiR-133b suppresses the proliferation, migration and invasion of lung adenocarcinoma cells by targeting SKA3.Cancer Biol. Ther.20212210-1257157810.1080/15384047.2021.1973819 34711122
    [Google Scholar]
  46. LiuD. SunZ. YeT. LiJ. ZengB. ZhaoQ. WangJ. XingH.R. The mitochondrial fission factor FIS1 promotes stemness of human lung cancer stem cells via mitophagy.FEBS Open Bio20211171997200710.1002/2211‑5463.13207 34051059
    [Google Scholar]
  47. LiuC. WuZ. WangL. YangQ. HuangJ. HuangJ. A mitophagy-related gene signature for subtype identification and prognosis prediction of hepatocellular carcinoma.Int. J. Mol. Sci.202223201212310.3390/ijms232012123 36292980
    [Google Scholar]
  48. LinX. YangM. HuangY. HuangX. ShiH. ChenB. KangJ. KeS. Gene signatures of endoplasmic reticulum stress and mitophagy for prognostic risk prediction in lung adenocarcinoma.IET Syst. Biol.202418310311710.1049/syb2.12092 38813617
    [Google Scholar]
  49. LiuW.S. LiR.M. LeY.H. ZhuZ.L. Construction of a mitophagy-related prognostic signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma.Heliyon20241015e3530510.1016/j.heliyon.2024.e35305 39170577
    [Google Scholar]
  50. DaiD. LiuL. GuoY. ShuiY. WeiQ. A comprehensive analysis of the effects of key mitophagy genes on the progression and prognosis of lung adenocarcinoma.Cancers (Basel)20221515710.3390/cancers15010057 36612054
    [Google Scholar]
  51. GuoL. JiC. GuS. YingK. ChengH. NiX. LiuJ. XieY. MaoY. Molecular cloning and characterization of a novel human kinase gene,PDIK1L.J. Genet.2003821-2273210.1007/BF02715878 14631099
    [Google Scholar]
  52. ZhaoW. HuangH. ZhaoZ. DingC. JiaC. WangY. WangG. LiY. LiuH. ChenJ. Identification of hypoxia and mitochondrial-related gene signature and prediction of prognostic model in lung adenocarcinoma.J. Cancer202415144513452610.7150/jca.97374 39006078
    [Google Scholar]
  53. LiuC. RuanY.Q. QuL.H. LiZ.H. XieC. PanY.Q. LiH.F. LiD.B. Prognostic modeling of lung adenocarcinoma based on hypoxia and ferroptosis-related genes.J. Oncol.2022202212510.1155/2022/1022580 36245988
    [Google Scholar]
  54. ShiX. KouM. DongX. ZhaiJ. LiuX. LuD. NiZ. JiangJ. CaiK. Integrative pan cancer analysis reveals the importance of CFTR in lung adenocarcinoma prognosis.Genomics2022114211027910.1016/j.ygeno.2022.110279 35134493
    [Google Scholar]
  55. KarantanouC. MinciacchiV.R. KumarR. ZanettiC. BravoJ. PereiraR.S. TascherG. TertelT. Covarrubias-PintoA. BankovK. PfeffermannL.M. BonigH. Divieti-PajevicP. McEwanD.G. GiebelB. MünchC. DikicI. KrauseD.S. Impact of mesenchymal stromal cell–derived vesicular cargo on B-cell acute lymphoblastic leukemia progression.Blood Adv.2023771190120310.1182/bloodadvances.2022007528 36044386
    [Google Scholar]
  56. LecorguilléM. NavarroP. ChenL.W. MurrinC. ViljoenK. MeheganJ. ShivappaN. HébertJ.R. KelleherC.C. SudermanM. PhillipsC.M. Maternal and paternal dietary quality and dietary inflammation associations with offspring DNA methylation and epigenetic biomarkers of aging in the lifeways cross-generation study.J. Nutr.202315341075108810.1016/j.tjnut.2023.01.028 36842935
    [Google Scholar]
  57. MengC. NaY. HanC. RenY. LiuM. MaP. BaiR. Exosomal miR-429 derived from adipose-derived stem cells ameliorated chondral injury in osteoarthritis via autophagy by targeting FEZ2.Int. Immunopharmacol.202312011031510.1016/j.intimp.2023.110315 37245297
    [Google Scholar]
  58. YangC. WangX. QiuC. ZhengZ. LinK. TuM. ZhangK. JiangK. GaoW. Identification of FEZ2 as a potential oncogene in pancreatic ductal adenocarcinoma.PeerJ20229e1273610.7717/peerj.12736 35036176
    [Google Scholar]
  59. ZhouW. ZhangJ. YanM. WuJ. LianS. SunK. LiB. MaJ. XiaJ. LianC. Orlistat induces ferroptosis-like cell death of lung cancer cells.Front. Med.202115692293210.1007/s11684‑020‑0804‑7 34085184
    [Google Scholar]
  60. BaiR. RebeloA. KleeffJ. SunamiY. Identification of prognostic lipid droplet-associated genes in pancreatic cancer patients via bioinformatics analysis.Lipids Health Dis.20212015810.1186/s12944‑021‑01476‑y 34078402
    [Google Scholar]
  61. RongL. XuY. ZhangK. JinL. LiuX. HNRNPA2B1 inhibited SFRP2 and activated Wnt-β/catenin via m6A-mediated miR-106b-5p processing to aggravate stemness in lung adenocarcinoma.Pathol. Res. Pract.202223315379410.1016/j.prp.2022.153794 35364458
    [Google Scholar]
  62. WangW. LiS. Upregulation of M6A reader HNRNPA2B1 associated with poor prognosis and tumor progression in lung adenocarcinoma.Recent Patents Anticancer Drug Discov.202419565266510.2174/0115748928258696230925064550 37877146
    [Google Scholar]
  63. ChenC. HuangL. SunQ. YuZ. WangX. BuL. HNRNPA2B1 demonstrates diagnostic and prognostic values based on pan-cancer analyses.Comput. Math. Methods Med.2022202211010.1155/2022/9867660 35529270
    [Google Scholar]
  64. WarmackR.A. PangE.Z. PelusoE. LowensonJ.D. OngJ.Y. TorresJ.Z. ClarkeS.G. Human Protein- L -isoaspartate O -Methyltransferase Domain-Containing Protein 1 (PCMTD1) Associates with Cullin-RING Ligase Proteins.Biochemistry2022611087989410.1021/acs.biochem.2c00130 35486881
    [Google Scholar]
  65. LiuJ. MuraliT. YuT. LiuC. SivakumaranT.A. MoseleyH.N.B. ZhulinI.B. WeissH.L. DurbinE.B. EllingsonS.R. LiuJ. HuangB. HallahanB.J. HorbinskiC.M. HodgesK. NapierD.L. BocklageT. MuellerJ. VanderfordN.L. FardoD.W. WangC. ArnoldS.M. Characterization of squamous cell lung cancers from appalachian kentucky.Cancer Epidemiol. Biomarkers Prev.201928234835610.1158/1055‑9965.EPI‑17‑0984 30377206
    [Google Scholar]
  66. LiY. LiuY. WangK. XueD. HuangY. TanZ. ChenY. STK24 promotes progression of LUAD and modulates the immune microenvironment.Mediators Inflamm.2023202311010.1155/2023/8646088 37181807
    [Google Scholar]
  67. HuangN. LinW. ShiX. TaoT. STK24 expression is modulated by DNA copy number/methylation in lung adenocarcinoma and predicts poor survival.Future Oncol.201814222253226310.2217/fon‑2018‑0126 29557182
    [Google Scholar]
  68. DengJ. HouG. FangZ. LiuJ. LvX.D. Distinct expression and prognostic value of OTU domain containing proteins in non small cell lung cancer.Oncol. Lett.20191855417542710.3892/ol.2019.10883 31612050
    [Google Scholar]
  69. WangH. HanG. ChenJ. Heterogeneity of tumor immune microenvironment in malignant and metastatic change in LUAD is revealed by single-cell RNA sequencing.Aging (Albany NY)202315125339535410.18632/aging.204752 37335089
    [Google Scholar]
  70. WangY. ZhangC. JiC. JinW. HeX. YuS. GuoR. Molecular subtypes based on cuproptosis-related genes and immune profiles in lung adenocarcinoma.Front. Genet.202213100693810.3389/fgene.2022.1006938 36313439
    [Google Scholar]
  71. LiY. HuangH. JiangM. YuN. YeX. HuangZ. ChenL. Identification and validation of a hypoxia-immune signature for overall survival prediction in lung adenocarcinoma.Front. Genet.20221397527910.3389/fgene.2022.975279 36263421
    [Google Scholar]
  72. WculekS.K. CuetoF.J. MujalA.M. MeleroI. KrummelM.F. SanchoD. Dendritic cells in cancer immunology and immunotherapy.Nat. Rev. Immunol.202020172410.1038/s41577‑019‑0210‑z 31467405
    [Google Scholar]
  73. QiuZ. PangG. XuX. LinJ. WangP. Characteristics of mast cell infiltration in lung adenocarcinoma and its impact on prognosis.Discover Oncology202415120810.1007/s12672‑024‑01062‑5 38834833
    [Google Scholar]
  74. LiH. ChangX. WangH. PengB. WangJ. ZhangP. ZhangL. Identification of a prognostic index system and tumor immune infiltration characterization for lung adenocarcinoma based on mRNA molecular of pyroptosis.Front. Med. (Lausanne)2022993483510.3389/fmed.2022.934835 36186792
    [Google Scholar]
/content/journals/cac/10.2174/0115734110370974250117074604
Loading
/content/journals/cac/10.2174/0115734110370974250117074604
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

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