Prognostic implication of glycolysis related gene signature in non-small cell lung cancer

Yao, Jie; Li, Rui; Liu, Xiao; Zhou, Xi-Jia; Li, Jianping; Liu, Tingting; Chen, Huo; Qu, Yi-Qing

doi:10.7150/jca.50274

Cited by 8 publications

(5 citation statements)

References 58 publications

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“…It has also been previously shown that HMMR expression is regulated by the cell cycle, with peak expression between late G2 and early mitosis (24). The latest research also demonstrated that HMMR is a glycolysis-related gene as a potential prognostic marker in nonsmall cell lung cancer (25). HMMR is also an aberrantly expressed gene associated with the cell cycle in proliferating cells of patients with acute myeloid leukemia in vitro (26).…”

Section: Discussionmentioning

confidence: 91%

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers

et al. 2021

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BackgroundLung cancer is the third most frequently diagnosed cancer in the world, with lung adenocarcinoma (LUAD) as the most common pathological type. But studies on the predictive effect of a single gene on LUAD are limited. We aimed to discover new predictive markers for LUAD.MethodsDifferentially high-expressed genes at each stage were obtained from the TCGA and GTEx databases. The functions of these genes were investigated through GO enrichment and KEGG pathway analyses. Then, the key genes were selected by applying whole gene overall survival time. The expression of the key gene was studied in LUAD, and survival analysis was performed using Kaplan-Meier mapper, followed by univariate and multifactorial COX analysis. Finally, the gene expression and its prognostic significance in the pan-cancer were examined.ResultsA total of 10,106 DEGs were obtained from the two datasets. The top 266 differentially upregulated genes intersected with the top 1,497 overall survival-related genes, and 87 key genes were identified. High-expressed HMMR was associated with a poor prognosis of LUAD. Univariate and multifactorial Cox analysis showed that HMMR was an independent prognostic factor for LUAD patients. A high HMMR expression was strongly associated with the overall survival (OS) and disease-specific survival (DSS) in 11 cancer types and with poorer OS, DSS, and PFI in 10 cancer types.ConclusionHMMR may be an independent prognostic indicator and an important biomarker in diagnosing and predicting the survival of LUAD patients. Also, HMMR may be a key predictor of a variety of cancers.

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Section: Discussionmentioning

confidence: 91%

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers

et al. 2021

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“…According to previous studies, several prognostic models have been proposed based on NSCLC, lung adenocarcinoma, or lung squamous cell carcinoma [ 45 – 50 ]. However, there was almost no signature proposed based on advanced NSCLC, and the actual use of the former models might lead to fallacies due to this.…”

Section: Resultsmentioning

confidence: 99%

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3–4 NSCLC patients

Tian

Zeng

et al. 2022

BMC Med Genomics

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Background Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments. Method The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases. Results In this study, patients with stage 3–4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters. Conclusions Our study has not only depicted the landscape of different clusters of stage 3–4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

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“…A potential caveat of our approach is that we studied the relationships between just two glycolysis signatures and a single method for estimating immune cell abundance (ssGSEA). However, while numerous other glycolysis-related signatures have been described (87)(88)(89)(90)(91)(92)(93)(94)(95), these signatures are (i) mostly composed of genes that are not directly involved in glycolysis (such as COL5A1, HMMR, STC1, among others); and (ii) have been developed by their association with prognosis/survival rather than with the metabolic activity of tumors. We initially chose the glycolysis signature described by Palaskas, et al (52) given that (i) this signature was developed by the direct measurement of FDG uptake in cell lines and in patients; and (ii) this signature is composed solely by genes involved in glucose metabolism.…”

Section: Discussionmentioning

confidence: 99%

Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

et al. 2022

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Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3+ and CD8+ lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types.

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Prognostic implication of glycolysis related gene signature in non-small cell lung cancer

Cited by 8 publications

References 58 publications

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3–4 NSCLC patients

Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

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