Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

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Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10238-024-01390-4.

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Most cited references 57

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

Matthew D. Wilkerson, D. Hayes (2010)

Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Wenhao Xu: xwhao0407@163.com

Xiaohang Hao: larrybird007@163.com

Dingwei Ye: dwyelie@163.com

Hailiang Zhang: zhanghl918@163.com

Journal

Journal ID (nlm-ta): Clin Exp Med

Journal ID (iso-abbrev): Clin Exp Med

Title: Clinical and Experimental Medicine

Publisher: Springer International Publishing (Cham )

ISSN (Print): 1591-8890

ISSN (Electronic): 1591-9528

Publication date (Electronic): 6 July 2024

Publication date PMC-release: 6 July 2024

Publication date (Print): 2024

Volume: 24

Issue: 1

Electronic Location Identifier: 152

Affiliations

[1 ]Department of Urology, Fudan University Shanghai Cancer Center, ( https://ror.org/00my25942) Shanghai, 200032 People’s Republic of China

[2 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Oncology, Shanghai Medical College, , Fudan University, ; Shanghai, 200032 People’s Republic of China

[3 ]Shanghai Genitourinary Cancer Institute, Shanghai, 200032 People’s Republic of China

[4 ]Department of Urology, The Affiliated Taian City Central Hospital of Qingdao University, ( https://ror.org/04vsn7g65) Taian, 271000 People’s Republic of China

[5 ]Affiliated Hospital of Youjiang Medical University for Nationalities, ( https://ror.org/0358v9d31) Baise, 533000 People’s Republic of China

Article

Publisher ID: 1390

DOI: 10.1007/s10238-024-01390-4

PMC ID: 11227463

PubMed ID: 38970690

SO-VID: 47445f31-d050-46fb-ad06-99ce0cd9e8c5

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 20 April 2024

Date accepted : 5 June 2024

Funding

Funded by: "Fuqing Scholar" Student Scientific Research Program of Shanghai Medical College, Fudan University

Award ID: No. FQXZ202304A

Award Recipient : Shiqi Ye

Funded by: Shanghai Anticancer Association EYAS PROJECT

Award ID: SACA-CY23A02 and SACA-CY23C04

Award Recipient : Aihetaimujiang Anwaier Wenhao Xu

Funded by: Shanghai Municipal Health Bureau Project

Award ID: No. 2020CXJQ03

Award Recipient : Dingwei Ye

Funded by: FundRef http://dx.doi.org/10.13039/100018904, Beijing Xisike Clinical Oncology Research Foundation;

Award ID: No. Y-HR2020MS-0948

Award Recipient : Hailiang Zhang

Funded by: China Anti-Cancer Association- Hengrui PARP Nicotinamide Cancer Research Fund

Award ID: CETSDHRCORP252-4-021

Award Recipient : Hailiang Zhang

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: clear cell renal cell carcinoma,glutamine metabolism,tumor microenvironment,prognosis,immunotherapy response,aldh18a1

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ScienceOpen disciplines: Medicine

Keywords: clear cell renal cell carcinoma, glutamine metabolism, tumor microenvironment, prognosis, immunotherapy response, aldh18a1

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Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

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Gamma Delta T cells

Most cited references 57

clusterProfiler: an R package for comparing biological themes among gene clusters.

GSVA: gene set variation analysis for microarray and RNA-Seq data

ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

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