Molecular Characterization and Prognosis of Lactate-Related Genes in Lung Adenocarcinoma

Objective: To explore the lactate-related genes (LRGs) in lung adenocarcinoma (LUAD) by various methods, construct a prognostic model, and explore the relationship between lactate subtypes and the immune tumor microenvironment (TME). Methods: 24 LRGs were collected. The mutation landscape and the prognosis value of LRGs were explored by using The Cancer Genome Atlas (TCGA) data. Consensus clustering analysis was used for different lactate subtype identification. Based on the lactate subtypes, we explore the landscape of TME cell infiltration. A risk-score was calculated by using the LASSO-Cox analysis. A quantitative real-time PCR assay was utilized to validate the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients. Results: Comparing the normal samples, 18 LRGs were differentially expressed in tumor samples, which revealed that the differential expression of LRGs may be related to Copy Number Variation (CNV) alterations. The two distinct lactate subtypes were defined. Compared to patients in the LRGcluster A group, LUAD patients in the LRGcluster B group achieved better survival. The prognostic model was constructed based on differentially expressed genes (DEGs) via the LASSO-Cox analysis, which showed the accuracy of predicting the prognosis of LUAD patients using the ROC curve. A high-risk score was related to a high immune score, stromal score, and tumor mutation burden (TMB). Patients had better OS with low risk compared with those with high risk. The sensitivities of different risk groups to chemotherapeutic drugs were explored. Finally, the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients was verified via qRT-PCR. Conclusions: The lactate subtypes were independent prognostic biomarkers in LUAD. Additionally, the difference in the lactate subtypes was an indispensable feature for the individual TME. The comprehensive evaluation of the lactate subtypes in the single tumor would help us to understand the infiltration characteristics of TME and guide immunotherapy strategies.

[1]  Jiazhen Zhou,et al.  Identification of SRXN1 and KRT6A as Key Genes in Smoking-Related Non-Small-Cell Lung Cancer Through Bioinformatics and Functional Analyses , 2022, Frontiers in Oncology.

[2]  Haiyan Pan,et al.  KRT6A Promotes Lung Cancer Cell Growth and Invasion Through MYC-Regulated Pentose Phosphate Pathway , 2021, Frontiers in Cell and Developmental Biology.

[3]  J. Jia,et al.  The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer , 2021, Frontiers in Cell and Developmental Biology.

[4]  Yiqun Yu,et al.  Identification of key pseudogenes in nasopharyngeal carcinoma based on RNA-Seq analysis , 2021, BMC Cancer.

[5]  E. Jonasch,et al.  High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. , 2021, Annals of oncology : official journal of the European Society for Medical Oncology.

[6]  A. Jemal,et al.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.

[7]  Fan Li,et al.  Association between lactate dehydrogenase levels and oncologic outcomes in metastatic prostate cancer: A meta‐analysis , 2020, Cancer medicine.

[8]  Xiaomao Li,et al.  Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer , 2020, Aging.

[9]  Lan Zhang,et al.  Lactic acid promotes macrophage polarization through MCT-HIF1α signaling in gastric cancer. , 2020, Experimental cell research.

[10]  Pierre Sonveaux,et al.  Monocarboxylate transporters in cancer , 2019, Molecular metabolism.

[11]  P. Xing,et al.  Efficacy of Crizotinib for Advanced ALK-Rearranged Non-Small-Cell Lung Cancer Patients with Brain Metastasis: A Multicenter, Retrospective Study in China , 2019, Targeted Oncology.

[12]  Xinyan Wang,et al.  LncRNA MIR210HG promotes proliferation and invasion of non-small cell lung cancer by upregulating methylation of CACNA2D2 promoter via binding to DNMT1 , 2019, OncoTargets and therapy.

[13]  Yassen Assenov,et al.  Maftools: efficient and comprehensive analysis of somatic variants in cancer , 2018, Genome research.

[14]  U. Testa,et al.  Lung Cancers: Molecular Characterization, Clonal Heterogeneity and Evolution, and Cancer Stem Cells , 2018, Cancers.

[15]  Wei Zhang,et al.  Monocarboxylate transporters MCT1 and MCT4 are independent prognostic biomarkers for the survival of patients with clear cell renal cell carcinoma and those receiving therapy targeting angiogenesis. , 2018, Urologic oncology.

[16]  Steven J. M. Jones,et al.  The Immune Landscape of Cancer , 2018, Immunity.

[17]  Yue Xu,et al.  Tumor-derived lactate induces M2 macrophage polarization via the activation of the ERK/STAT3 signaling pathway in breast cancer , 2018, Cell cycle.

[18]  F. Askin,et al.  Current WHO guidelines and the critical role of immunohistochemical markers in the subclassification of non-small cell lung carcinoma (NSCLC): Moving from targeted therapy to immunotherapy. , 2017, Seminars in cancer biology.

[19]  Jamey D. Young,et al.  Lactate Metabolism in Human Lung Tumors , 2017, Cell.

[20]  U. Martinez-outschoorn,et al.  Metabolic coupling and the Reverse Warburg Effect in cancer: Implications for novel biomarker and anticancer agent development. , 2017, Seminars in oncology.

[21]  F. Baltazar,et al.  Value of pH regulators in the diagnosis, prognosis and treatment of cancer. , 2017, Seminars in cancer biology.

[22]  E. Bandrés,et al.  Clinical relevance of the transcriptional signature regulated by CDC42 in colorectal cancer , 2017, Oncotarget.

[23]  Pierre Sonveaux,et al.  Monocarboxylate transporters in the brain and in cancer☆ , 2016, Biochimica et biophysica acta.

[24]  P. Jänne,et al.  Five‐Year Survival in EGFR‐Mutant Metastatic Lung Adenocarcinoma Treated with EGFR‐TKIs , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[25]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[26]  A. Longatto-Filho,et al.  Reprogramming energy metabolism and inducing angiogenesis: co-expression of monocarboxylate transporters with VEGF family members in cervical adenocarcinomas , 2015, BMC Cancer.

[27]  G. Christofori,et al.  Targeting Metabolic Symbiosis to Overcome Resistance to Anti-angiogenic Therapy , 2015, Cell reports.

[28]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[29]  N. Hacohen,et al.  Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity , 2015, Cell.

[30]  Paul Geeleher,et al.  pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels , 2014, PloS one.

[31]  A. Halestrap Monocarboxylic acid transport. , 2013, Comprehensive Physiology.

[32]  G. Getz,et al.  Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.

[33]  J. Pouysségur,et al.  Disrupting proton dynamics and energy metabolism for cancer therapy , 2013, Nature Reviews Cancer.

[34]  L. de Geus-Oei,et al.  Differences in metabolism between adeno- and squamous cell non-small cell lung carcinomas: spatial distribution and prognostic value of GLUT1 and MCT4. , 2012, Lung cancer.

[35]  Franziska Hirschhaeuser,et al.  Lactate: a metabolic key player in cancer. , 2011, Cancer research.

[36]  Matthew D. Wilkerson,et al.  ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking , 2010, Bioinform..

[37]  L. Cantley,et al.  Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation , 2009, Science.

[38]  Igor Jurisica,et al.  Gene expression–based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study , 2008, Nature Medicine.

[39]  G. Raj,et al.  How to build and interpret a nomogram for cancer prognosis. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[41]  O. Warburg [Origin of cancer cells]. , 1956, Oncologia.

[42]  F. Baltazar,et al.  Lactate and Lactate Transporters as Key Players in the Maintenance of the Warburg Effect. , 2020, Advances in experimental medicine and biology.