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      Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

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          Abstract

          Background

          Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

          Methods

          The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models.

          Results

          Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis.

          Conclusion

          In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12885-024-12251-4.

          Graphical Abstract

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12885-024-12251-4.

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          Most cited references62

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)
          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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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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              WGCNA: an R package for weighted correlation network analysis

              Peter Langfelder, Steve Horvath (2008)
              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Yuqin Pan: panyuqin01@163.com
                Shukui Wang: sk_wang@njmu.edu.cn
                Journal
                Journal ID (nlm-ta): BMC Cancer
                Journal ID (iso-abbrev): BMC Cancer
                Title: BMC Cancer
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2407
                Publication date (Electronic): 23 April 2024
                Publication date PMC-release: 23 April 2024
                Publication date Collection: 2024
                Volume: 24
                Electronic Location Identifier: 516
                Affiliations
                [1 ]School of Medicine, Southeast University, ( https://ror.org/04ct4d772) 210009 Nanjing, Jiangsu China
                [2 ]General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, ( https://ror.org/059gcgy73) No. 68, Changle Road, 210006 Nanjing, Jiangsu China
                [3 ]School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, ( https://ror.org/01sfm2718) 211122 Nanjing, Jiangsu China
                [4 ]Jiangsu Collaborative Innovation Center on Cancer Personalized Medicine, Nanjing Medical University, ( https://ror.org/059gcgy73) 211100 Nanjing, Jiangsu China
                Article
                Publisher ID: 12251
                DOI: 10.1186/s12885-024-12251-4
                PMC ID: 11041013
                PubMed ID: 38654221
                SO-VID: bf4b1edf-b454-473b-8f69-6b4cfba566d8
                Copyright © © The Author(s) 2024
                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 29 December 2023
                Date accepted : 11 April 2024
                Funding
                Funded by: Postgraduate Research & Practice Innovation Program of Jiangsu Province
                Award ID: JX12014127
                Funded by: National Natural Science Foundation of China
                Award ID: 82272629, 81972806, 82203489
                Funded by: Key projects of Health Science and technology development in Nanjing
                Award ID: ZKX21042
                Funded by: Jiangsu Provincial Key Research and Development Plan
                Award ID: BE2019614
                Funded by: Jiangsu Provincial Medical Key Discipline Cultivation Unit
                Award ID: JSDW202239
                Funded by: Elderly Health Research Project of Jiangsu Province
                Award ID: LR2021017
                Funded by: Specialized Cohort Research Project of Nanjing Medical University
                Award ID: NMUC2020035, NMUC2021013A
                Funded by: Jiangsu Cancer Personalized Medicine Collaborative Innovation Center
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2024

                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: colorectal cancer (crc),cancer associated fibroblasts (cafs),machine learning,prognosis
                Data availability:
                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: colorectal cancer (crc), cancer associated fibroblasts (cafs), machine learning, prognosis

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