Correlation between SMADs and Colorectal Cancer Expression, Prognosis, and Immune Infiltrates

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Abstract

Background

In recent years, the incidence and mortality of colorectal cancer (CRC) are increasing, and the 5-year survival rate of advanced metastatic CRC is poor. Small mothers against decapentaplegic (SMAD) superfamily are intracellular signal transduction proteins associated with the development and prognosis of a variety of tumors. At present, no study has systematically analysed the relationship between SMADs and CRC.

Methods

Here, R3.6.3 was used to analyse the expression of SMADs in pan-cancer and CRC. Protein expression of SMADs were analysed by Human Protein Atlas (HPA). Gene expression profiling interactive analysis (GEPIA) was used to evaluate the correlation between SMADs and tumor stage in CRC. The effect of R language and GEPIA on prognosis was analysed. Mutation rates of SMADs in CRC were determined by cBioPortal, and potentially related genes were predicted using GeneMANIA. R analysis was used to correlate immune cell infiltration in CRC.

Results

Both SMAD1 and SMAD2 were found to be weakly expressed in CRC and correlated with the immune invasion level. SMAD1 was correlated with patient prognosis, and SMAD2 was correlated with tumor stage. SMAD3, SMAD4, and SMAD7 were all expressed at low levels in CRC and associated with a variety of immune cells. SMAD3 and SMAD4 proteins were also expressed at low levels, and SMAD4 had the highest mutation rate. SMAD5 and SMAD6 were overexpressed in CRC, and SMAD6 was also associated with patient overall survival (OS) and CD8+ T cells, macrophages, and neutrophils.

Conclusions

Our results reveal innovative and strong evidence that SMADs can be used as biomarkers for the treatment and prognosis of CRC.

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Most cited references 34

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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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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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GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

Zefang Tang, Chenwei Li, Boxi Kang … (2017)

Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

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Author and article information

Contributors

Yongheng He:

ORCID: https://orcid.org/0000-0002-0751-5125

Journal

Journal ID (nlm-ta): Int J Anal Chem

Journal ID (iso-abbrev): Int J Anal Chem

Journal ID (publisher-id): ijac

Title: International Journal of Analytical Chemistry

Publisher: Hindawi

ISSN (Print): 1687-8760

ISSN (Electronic): 1687-8779

Publication date Collection: 2023

Publication date (Electronic): 8 March 2023

Volume: 2023

Electronic Location Identifier: 8414040

Affiliations

¹Hunan University of Chinese Medicine, Changsha, Hunan 410208, China

²Department of Anorectal Surgery, Chenzhou NO. 1 People's Hospital, Chenzhou 423000, China

³Department of Anorectal Surgery, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan 410005, China

⁴Department of Anorectal Surgery, The Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine, Changsha, Hunan 410006, China

Author notes

Academic Editor: Ammar AL-Farga

Author information

Hongbiao Luo https://orcid.org/0009-0002-8137-9843

Tao Zhang https://orcid.org/0009-0009-5883-0991

Tianshu Peng https://orcid.org/0009-0006-6588-9922

Yanru Yao https://orcid.org/0009-0008-6771-555X

Yongheng He https://orcid.org/0000-0002-0751-5125

Article

DOI: 10.1155/2023/8414040

PMC ID: 10038740

PubMed ID: 36969909

SO-VID: 90db842e-e159-4093-88f8-3e9f91ede48c

License:

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 7 October 2022

Date revision received : 20 January 2023

Date accepted : 3 February 2023

Funding

Funded by: Key Project of Hunan Administration of Traditional Chinese Medicine

Award ID: 2021017

Funded by: Natural Science Foundation of Hunan Province

Award ID: 2021JJ30419

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Correlation between SMADs and Colorectal Cancer Expression, Prognosis, and Immune Infiltrates

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Most cited references 34

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

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

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

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