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PMID: 39287231 Published · ppublish English Journal Article

An abnormal metabolism-related gene, ALG3, is a potential diagnostic and prognostic biomarker for lung adenocarcinoma.

Medicine ·Vol. 103 ·No. 37 ·2024-09-13 ·Pages e38746

Reyimu A, Cheng X, Liu W, Kaisaier A, Wang X, Sha Y, Guo R, Paerhati P, Maimaiti M, He C, Li L, Zou X, Xu A

Abstract

To explore the abnormal metabolism-related genes that affect the prognosis of patients with lung adenocarcinoma (LUAD), and analyze the relationship with immune infiltration and competing endogenous RNA (ceRNA) network. Transcriptome data of LUAD were downloaded from the Cancer Genome Atlas database. Abnormal metabolism-related differentially expressed genes in LUAD were screened by the R language. Cox analysis was used to construct LUAD prognostic risk model. Kaplan-Meier test, ROC curve and nomograms were used to evaluate the predictive ability of metabolic related gene prognostic model. CIBERSORT algorithm was used to analyze the relationship between risk score and immune infiltration. The starBase database constructed a regulatory network consistent with the ceRNA hypothesis. IHC experiments were performed to verify the differential expression of ALG3 in LUAD and paracancerous samples. In this study, 42 abnormal metabolism-related differential genes were screened. After survival analysis, the final 5 metabolism-related genes were used as the construction of prognosis model, including ALG3, COL7A1, KL, MST1, and SLC52A1. In the model, the survival rate of LUAD patients in the high-risk subgroup was lower than that in the low-risk group. In addition, the risk score of the constructed LUAD prognostic model can be used as an independent prognostic factor for patients. According to the analysis of CIBERSORT algorithm, the risk score is related to the infiltration of multiple immune cells. The potential ceRNA network of model genes in LUAD was constructed through the starBase database. IHC experiments revealed that ALG3 expression was upregulated in LUAD. The prognostic model of LUAD reveals the relationship between metabolism and prognosis of LUAD, and provides a novel perspective for diagnosis and research of LUAD.

MeSH Terms
Humans Biomarkers, Tumor/genetics,metabolism Adenocarcinoma of Lung/genetics,mortality,metabolism Prognosis Lung Neoplasms/genetics,mortality,metabolism,diagnosis Male Nomograms Female Gene Expression Regulation, Neoplastic Kaplan-Meier Estimate Middle Aged Transcriptome ROC Curve
Chemicals
Biomarkers, Tumor
Authors & Affiliations
13 authors, click to expand affiliations / ORCID
Reyimu Abdusemer ORCID
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Cheng Xiang
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Liu Wen
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Kaisaier Aihemaitijiang
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Wang Xinying
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Sha Yinzhong
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Guo Ruijie
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Paerhati Pawuziye
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Maimaiti Maimaituxun
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
He Chuanjiang
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
Li Li
The First People's Hospital of Kashi, Kashi City, China.
Zou Xiaoguang
The First People's Hospital of Kashi, Kashi City, China.
Xu Aimin
Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
References (27)
27 references, click to expand
  1. Abu RF, Singhi EK, Sridhar A, Faisal MS, Desai A. Lung cancer treatment advances in 2022. Cancer Invest. 2023;41:12–24.
  2. Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol. 2023;20:624–39.
  3. Wang S, Lv J, Lv J, et al. Prognostic value of lactate dehydrogenase in non-small cell lung cancer patients with brain metastases: a retrospective cohort study. J Thorac Dis. 2022;14:4468–81.
  4. Lin X, Xiao Z, Chen T, Liang SH, Guo H. Glucose metabolism on tumor plasticity, diagnosis, and treatment. Front Oncol. 2020;10:317.
  5. Xia L, Oyang L, Lin J, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20:28.
  6. Zhao L, Mao Y, Zhao Y, Cao Y, Chen X. Role of multifaceted regulators in cancer glucose metabolism and their clinical significance. Oncotarget. 2016;7:31572–85.
  7. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.
  8. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284–7.
  9. Reznik E, Sander C. Extensive decoupling of metabolic genes in cancer. PLoS Comput Biol. 2015;11:e1004176.
  10. Zhang Y, Qin W, Zhang W, Qin Y, Zhou YL. Guidelines on lung adenocarcinoma prognosis based on immuno-glycolysis-related genes. Clin Transl Oncol. 2023;25:959–75.
  11. Ke SB, Qiu H, Chen JM, et al. ALG3 contributes to the malignancy of non-small cell lung cancer and is negatively regulated by MiR-98-5p. Pathol Res Pract. 2020;216:152761.
  12. Oh SE, Oh MY, An JY, et al. Prognostic value of highly expressed type VII collagen (COL7A1) in patients with gastric cancer. Pathol Oncol Res. 2021;27:1609860.
  13. Pan KH, Yao L, Chen Z, et al. KL is a favorable prognostic factor related immune for clear cell renal cell carcinoma. Eur J Med Res. 2023;28:356.
  14. Zhang W, Liu K, Pei Y, Ma J, Tan J, Zhao J. Mst1 regulates non-small cell lung cancer A549 cell apoptosis by inducing mitochondrial damage via ROCK1/F-actin pathways. Int J Oncol. 2018;53:2409–22.
  15. Zhang X, Shi X, Zhao H, Jia X, Yang Y. Identification and validation of a tumor microenvironment-related gene signature for prognostic prediction in advanced-stage non-small-cell lung cancer. Biomed Res Int. 2021;2021:8864436.
  16. Lin B, Du L, Li H, Zhu X, Cui L, Li X. Tumor-infiltrating lymphocytes: warriors fight against tumors powerfully. Biomed Pharmacother. 2020;132:110873.
  17. Thomopoulou K, Papadaki C, Monastirioti A, et al. MicroRNAs regulating tumor immune response in the prediction of the outcome in patients with breast cancer. Front Mol Biosci. 2021;8:668534.
  18. Chen J, Tan Y, Sun F, et al. Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer. Genome Biol. 2020;21:152.
  19. Zhou X, Zhao S, He Y, Geng S, Shi Y, Wang B. Precise spatiotemporal interruption of regulatory T-cell-mediated CD8(+) T-cell suppression leads to tumor immunity. Cancer Res. 2019;79:585–97.
  20. Li M, Zhao J, Yang R, et al. CENPF as an independent prognostic and metastasis biomarker corresponding to CD4+ memory T cells in cutaneous melanoma. Cancer Sci. 2022;113:1220–34.
  21. Wang H, Wang X, Li X, et al. CD68(+)HLA-DR(+) M1-like macrophages promote motility of HCC cells via NF-κB/FAK pathway. Cancer Lett. 2014;345:91–9.
  22. Xiao M, Zhang J, Chen W, Chen W. M1-like tumor-associated macrophages activated by exosome-transferred THBS1 promote malignant migration in oral squamous cell carcinoma. J Exp Clin Cancer Res. 2018;37:143.
  23. Hajizadeh F, Aghebati ML, Alexander M, et al. Tumor-associated neutrophils as new players in immunosuppressive process of the tumor microenvironment in breast cancer. Life Sci. 2021;264:118699.
  24. Cupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18:360.
  25. Zhang K, Zhang L, Mi Y, et al. A ceRNA network and a potential regulatory axis in gastric cancer with different degrees of immune cell infiltration. Cancer Sci. 2020;111:4041–50.
  26. Chan JJ, Tay Y. Noncoding RNA:RNA regulatory networks in cancer. Int J Mol Sci . 2018;19:1310.
  27. Swain AC, Mallick B. miRNA-mediated “tug-of-war” model reveals ceRNA propensity of genes in cancers. Mol Oncol. 2018;12:855–68.
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Article Info
Journal
Medicine
Abbr.
Medicine (Baltimore)
ISSN
1536-5964
DOI
10.1097/MD.0000000000038746
Published
2024-09-13
Pages
e38746
Language
English
Region
United States
NLM ID
2985248R
PMCID
PMC11404934
Subset
IM
Grants
Analysis of the Causes of tuberculosis High Incidence in Kashgar region and key technology Development for Artificial-Intelligence-based Discrimination of Imaging Big Data · 2022B03032-1
External Links
PubMed source DOI: 10.1097/MD.0000000000038746 PMC Full Text View journal details
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