Correspondence to editorial on “Development and validation of a stromal-immune signature to predict prognosis in intrahepatic cholangiocarcinoma” Yu-Hang Ye, Shao-Lai Zhou Clinical and Molecular Hepatology.2025; 31(1): e90. CrossRef
Correspondence to editorial 1 on “Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: insights from the IMbrave150 Trial” Sung Hwan Lee, Sun Young Yim, Ji Hoon Kim, Sunyoung S. Lee, Ahmed O. Kaseb, Ju-Seog Lee Clinical and Molecular Hepatology.2025; 31(1): e81. CrossRef
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Backgrounds/Aims Intrahepatic cholangiocarcinoma (ICC) is a highly desmoplastic tumor with poor prognosis even after curative resection. We investigated the associations between the composition of the ICC stroma and immune cell infiltration and aimed to develop a stromal-immune signature to predict prognosis in surgically treated ICC.
Methods We recruited 359 ICC patients and performed immunohistochemistry to detect α-smooth muscle actin (α-SMA), CD3, CD4, CD8, Foxp3, CD68, and CD66b. Aniline was used to stain collagen deposition. Survival analyses were performed to detect prognostic values of these markers. Recursive partitioning for a discrete-time survival tree was applied to define a stromal-immune signature with distinct prognostic value. We delineated an integrated stromal-immune signature based on immune cell subpopulations and stromal composition to distinguish subgroups with different recurrence-free survival (RFS) and overall survival (OS) time.
Results We defined four major patterns of ICC stroma composition according to the distributions of α-SMA and collagen: dormant (α-SMAlow/collagenhigh), fibrogenic (α-SMAhigh/collagenhigh), inert (α-SMAlow/collagenlow), and fibrolytic (α-SMAhigh/collagenlow). The stroma types were characterized by distinct patterns of infiltration by immune cells. We divided patients into six classes. Class I, characterized by high CD8 expression and dormant stroma, displayed the longest RFS and OS, whereas Class VI, characterized by low CD8 expression and high CD66b expression, displayed the shortest RFS and OS. The integrated stromal-immune signature was consolidated in a validation cohort.
Conclusions We developed and validated a stromal-immune signature to predict prognosis in surgically treated ICC. These findings provide new insights into the stromal-immune response to ICC.
Citations
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Development and validation of a biopsy-based model using tumor-associated neutrophils to predict neoadjuvant chemotherapy response in osteosarcoma: a large single-center retrospective cohort study Wanjiang Feng, Zibo Xu, Ziming Yan, Hongyu Wu, Haoyu Guo, Yuejun Luo, Weifeng Liu Journal of Bone Oncology.2026; 58: 100764. CrossRef
Predictive Immunohistochemistry in Cholangiocarcinoma: Current Clinical Utility, Practical Limitations, and Emerging Directions Francesco Vasuri, Alessandra Boccaccino, Stefano Tamberi, Luca Saragoni APMIS.2026;[Epub] CrossRef
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The intratumoral balance of IgG4+ plasma cells and CD8+ T cells is associated with prognosis of intrahepatic cholangiocarcinoma after curative resection Yu-Hang Ye, Hao-Yang Xin, Ning Li, Chu-Bin Luo, Long Chen, Jing-Yue Pan, Ye Xu, Fan Weng, Cun-Yang Tu, Ya-Ya Ji, Jia Fan, Jian Zhou, Zheng-Jun Zhou, Shao-Lai Zhou Digestive and Liver Disease.2025; 57(7): 1487. CrossRef
A blood test-based nomogram to predict the progression-free survival of patients with intrahepatic cholangiocarcinoma after surgical resection Lirong Peng, Yang Shi, Shuang Yang, Cunyan Li Frontiers in Oncology.2025;[Epub] CrossRef
Prognostic impact of tumor microenvironment characteristics in intrahepatic cholangiocarcinoma: A systematic review and meta-analysis Zaiba Shafik Dawood, Mujtaba Khalil, Arsalan Javid, Abdul Ali, Usama Waqar, Zayan Alidina, Illiyun Banani, Timothy M. Pawlik Surgical Oncology.2025; 63: 102307. CrossRef
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Background/Aims Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression.
Methods Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD.
Results After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort.
Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.
Citations
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