Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

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Abstract

Background

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

Methods

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

Results

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

Conclusions

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-024-06011-y.

Related collections

Most cited references 86

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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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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P Tamayo, V K Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Is Open Access

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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All references

Author and article information

Contributors

Chao Shen: shenchao@whu.edu.cn

You Zhou: zhouy58@cardiff.ac.uk

Xin Du: dongxin@hbfy.com

Junbo Hu:

ORCID: http://orcid.org/0000-0003-2606-8591

cqjbhu@163.com

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 31 December 2024

Publication date PMC-release: 31 December 2024

Publication date Collection: 2024

Volume: 22

Electronic Location Identifier: 1163

Affiliations

[1 ]Tongji Medical College, Maternal and Child Health Hospital of Hubei Province, Huazhong University of Science and Technology, ( https://ror.org/00p991c53) Wuhan, Hubei Province 430070 China

[2 ]State Key Laboratory of Virology, College of Life Sciences and Frontier Science Center for Immunology and Metabolism, RNA Institute, Wuhan University, ( https://ror.org/033vjfk17) Wuhan, 430072 China

[3 ]Animal Bio-Safety Level III Laboratory/Institute for Vaccine Research, Taikang Medical School (School of Basic Medical Sciences), Wuhan University, ( https://ror.org/033vjfk17) Wuhan, 430071 China

[4 ]Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, ( https://ror.org/033vjfk17) Wuhan, Hubei Province 430072 China

[5 ]Systems Immunity Research Institute, Cardiff University, ( https://ror.org/03kk7td41) Cardiff, CF14 4XN UK

[6 ]Division of Infection and Immunity, School of Medicine, Cardiff University, ( https://ror.org/03kk7td41) Cardiff, CF14 4XN UK

Author information

Junbo Hu http://orcid.org/0000-0003-2606-8591

Article

Publisher ID: 6011

DOI: 10.1186/s12967-024-06011-y

PMC ID: 11687147

PubMed ID: 39741285

SO-VID: a46a125e-5148-4b66-8b8b-2f9b5eee9bc1

License:

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

History

Date received : 5 August 2024

Date accepted : 18 December 2024

Funding

Funded by: the National Key R&D Program of China

Award ID: 2021YFF0702000

Award Recipient : Yu Chen

Funded by: Open Research Fund Program of the State Key Laboratory of Virology of China

Award ID: 2021KF005

Award Recipient : Limin Zhou

Funded by: Knowledge Innovation Program of Wuhan -Basic Research

Award ID: Knowledge Innovation Program of Wuhan -Basic Research

Award Recipient : Li Zhou

Funded by: FundRef http://dx.doi.org/10.13039/501100003819, Natural Science Foundation of Hubei Province;

Award ID: 2024AFB906

Award Recipient : Li Zhou

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: cervical squamous cell carcinoma (cscc),spatial transcriptomics (st),app,trps1,tumor metabolism

Data availability: