Evidence map›Paper›PMID 41979818›Full record

ArticleDiscover oncology2026

Exploration of the roles of SSR2 in hepatocellular carcinogenesis based on single-cell transcriptomics and spatial transcriptomics.

Siyuan Liu, Xuyang Wang, Donghao Cheng, Kaipeng Hu, Xihu Qin

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Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Siyuan Liu *Department of Hepato-Biliary-Pancreatic Surgery, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Xuyang Wang *The First Clinical Medical College of Anhui Medical University, Hefei, 230032, China.
Donghao Cheng *The First Clinical Medical College of Anhui Medical University, Hefei, 230032, China.
Kaipeng Hu *The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xihu QinDepartment of Hepato-Biliary-Pancreatic Surgery, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China. qinxihu@126.com.

Funding

Postgraduate Research & Practice Innovation Program of Jiangsu Province Grant No. SJCX24_0745
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pathogenesis and limitations of therapeutic approaches.

methodTo improve the prognosis of HCC patients, we analyzed single-cell transcriptome and spatial transcriptome on liver tissues from HCC patients. We applied Non-negative Matrix Factorization (NMF) method on spatial transcriptome and found a critical pathway associated with HCC through GO over-representation analysis. According to the pathway activity score, the cells in single-cell transcriptome were divided into three groups. Then we conducted hdWGCNA and selected HCC-related co-expressed gene modules which showed significant intergroup differences. Integrative machine learning algorithms on bulk transcriptome were employed for core genes selection. We respectively evaluated their AUC values as independent diagnostic markers and retained a core gene. We conducted a survival analysis using this gene. Next, we performed immune infiltration analysis, tumor microenvironmental analysis and drug sensitivity analysis. Finally, pseudotime analysis, differentiation potential analysis and cell-cell communication analysis were applied to decipher the mechanisms underlying tumor initiation and progression.

resultsSingle-cell transcriptome analysis revealed the high heterogeneity of cells in patients with HCC. The GO over-representation analysis based on NMF indicated that cytoplasmic translation path was enriched in most NMF components. The SSR2 gene passed the integrative machine learning algorithms and possessed excellent diagnosis (AUC > 0.8) and prognostic abilities (K-M curve, P = 0.007). Further analysis identified SSR2 as the top-ranked gene by average expression level in NK cells compared to all other immune cell clusters. Its expression showed a consistent downward trend during NK cell maturation.NK cells with high expression of SSR2 exhibited stronger cell-cell communication than those with low expression of SSR2. NK cells mainly communicated with other cells through MIF-mediated signaling pathway.

conclusionIn hepatocellular carcinoma (HCC), the high expression of the SSR2 gene in NK cells is accompanied by enhanced cytoplasmic translation pathway, which promotes MIF secretion and activates its downstream signaling pathways, thereby driving the disruption of the tumor immune microenvironment and the progression of the disease.

Indexed as

Cytoplasmic translationHepatocellular carcinomaMachine learningMIFNatural killer cellsSingle-cell transcriptomeSpatial transcriptomeSSR2Tumor immune evasion

Identifiers

PMID41979818
PMCPMC13201726

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