Background Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, with poor prognosis and immune checkpoint inhibitor (ICI) resistance mediated by immune escape. Senescence-related immunosuppressive genes (SRIGs) regulate the tumor microenvironment (TME); however, their hub genes and prognostic value in GC remain unclear. This study aimed to identify hub SRIGs and explore their significance. Methods Transcriptomic, single-cell RNA sequencing, and spatial transcriptomic data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and Pearson correlation analysis were used to screen for SRIGs. Eight machine learning algorithms identified the hub SRIG, which was validated by RT-qPCR, immunohistochemistry, and clinical samples. Pseudotime trajectory, CellChat, and molecular docking analyses were used to explore the underlying mechanisms and potential drugs. A pan-cancer analysis was used to verify its generalizability. Results A senescence-related immunosuppressive signature was constructed, stratifying GC into two subtypes with distinct TME and prognosis. Microfibril-associated protein 2 (MFAP2) was identified as the hub SRIG, significantly upregulated in GC, and an independent prognostic risk factor. It was specifically expressed in extracellular matrix cancer-associated fibroblasts (eCAFs), driving their differentiation and extracellular matrix (ECM) remodeling. MFAP2⁺ eCAFs promoted M2 macrophage polarization via CSF1-CSF1R and C3-C3AR1 axes, mediating immune escape and ICI resistance. AZD4547 and MK-2461 were potential targeted drugs. Pan-cancer analysis confirmed the ubiquitous oncogenic role of MFAP2. Conclusions MFAP2 is a hub SRIG linking eCAFs, ECM remodeling, and immune escape in GC. It serves as a reliable prognostic and ICI resistance predictor and potential therapeutic target, providing a basis for precision therapy in GC.

Senescence-related immunosuppressive gene MFAP2 orchestrates stromal-immune crosstalk and immunotherapy resistance in gastric cancer

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2026年8月4日

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Yichan Hu,
Liaoxiang Zhu,
Lijun Zhou,
Yichi Zhang,
Shunjie Zheng,
Jiani Zhu,
Ziyu Xi,
Zhan Yang,
Yifei Hu

摘要

Background

Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, with poor prognosis and immune checkpoint inhibitor (ICI) resistance mediated by immune escape. Senescence-related immunosuppressive genes (SRIGs) regulate the tumor microenvironment (TME); however, their hub genes and prognostic value in GC remain unclear. This study aimed to identify hub SRIGs and explore their significance.

Methods

Transcriptomic, single-cell RNA sequencing, and spatial transcriptomic data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and Pearson correlation analysis were used to screen for SRIGs. Eight machine learning algorithms identified the hub SRIG, which was validated by RT-qPCR, immunohistochemistry, and clinical samples. Pseudotime trajectory, CellChat, and molecular docking analyses were used to explore the underlying mechanisms and potential drugs. A pan-cancer analysis was used to verify its generalizability.

Results

A senescence-related immunosuppressive signature was constructed, stratifying GC into two subtypes with distinct TME and prognosis. Microfibril-associated protein 2 (MFAP2) was identified as the hub SRIG, significantly upregulated in GC, and an independent prognostic risk factor. It was specifically expressed in extracellular matrix cancer-associated fibroblasts (eCAFs), driving their differentiation and extracellular matrix (ECM) remodeling. MFAP2⁺ eCAFs promoted M2 macrophage polarization via CSF1-CSF1R and C3-C3AR1 axes, mediating immune escape and ICI resistance. AZD4547 and MK-2461 were potential targeted drugs. Pan-cancer analysis confirmed the ubiquitous oncogenic role of MFAP2.

Conclusions

MFAP2 is a hub SRIG linking eCAFs, ECM remodeling, and immune escape in GC. It serves as a reliable prognostic and ICI resistance predictor and potential therapeutic target, providing a basis for precision therapy in GC.