Single-Cell and Multi-Omics-Based Characterization of Gastric Cancer Identifies TPP1 as a Potential Target for Gastric Cancer Progression and Treatment

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yingying Zhao · Jiakang Ma · Rujin Huang · Shuxian Pan

3 April 2026 at 18:00

Oncol Res. 2026 Mar 23;34(4):27. doi: 10.32604/or.2026.070208. eCollection 2026.

ABSTRACT

BACKGROUND: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression; however, their contribution to the functional classification and personalized treatment of gastric cancer remains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatment strategies for patients with gastric cancer.

METHODS: Single-cell and bulk transcriptomic analyses were integrated to characterize gastric cancer fibroblasts. "Seurat", "Slingshot", and "CellChat" were used for dimensionality reduction, trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast modules were identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct a prognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features. The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays.

RESULTS: Eight fibroblast subpopulations associated with gastric cancer metastasis exhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated that metastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed marked immunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-related pathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressed gastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis.

CONCLUSION: This study characterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis and established a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strong predictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, the findings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.

PMID:41930144 | PMC:PMC13040347 | DOI:10.32604/or.2026.070208

Author Correction: Gut stem cell necroptosis by genome instability triggers bowel inflammation

Nature - Issue - nature.com science feeds

By: Ruicong Wang · Hongda Li · Jianfeng Wu · Zhi-Yu Cai · Baizhou Li · Hengxiao Ni · Xingfeng Qiu · Hui Chen · Wei Liu · Zhang-Hua Yang · Min Liu · Jin Hu · Yaoji Liang · Ping Lan · Jiahuai Han · Wei Mo

11 March 2026 at 08:00

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10302-3

Author Correction: Gut stem cell necroptosis by genome instability triggers bowel inflammation

(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork

cs.AI, q-bio.NC updates on arXiv.org

By: Tianjin Huang · Fang Meng · Li Shen · Fan Liu · Yulong Pei · Mykola Pechenizkiy · Shiwei Liu · Tianlong Chen

24 February 2026 at 13:00

arXiv:2407.17412v2 Announce Type: replace-cross Abstract: Large-scale neural networks have demonstrated remarkable performance in different domains like vision and language processing, although at the cost of massive computation resources. As illustrated by compression literature, structural model pruning is a prominent algorithm to encourage model efficiency, thanks to its acceleration-friendly sparsity patterns. One of the key questions of structural pruning is how to estimate the channel significance. In parallel, work on data-centric AI has shown that prompting-based techniques enable impressive generalization of large language models across diverse downstream tasks. In this paper, we investigate a charming possibility - \textit{leveraging visual prompts to capture the channel importance and derive high-quality structural sparsity}. To this end, we propose a novel algorithmic framework, namely \texttt{PASS}. It is a tailored hyper-network to take both visual prompts and network weight statistics as input, and output layer-wise channel sparsity in a recurrent manner. Such designs consider the intrinsic channel dependency between layers. Comprehensive experiments across multiple network architectures and six datasets demonstrate the superiority of \texttt{PASS} in locating good structural sparsity. For example, at the same FLOPs level, \texttt{PASS} subnetworks achieve $1\%\sim 3\%$ better accuracy on Food101 dataset; or with a similar performance of $80\%$ accuracy, \texttt{PASS} subnetworks obtain $0.35\times$ more speedup than the baselines.

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