Cancer Research and Treatment

Original Articles

Hematologic malignancy

Literature-Guided 6-Gene Signature for the Stratification of High-Risk Acute Myeloid Leukemia: Jong Keon Song, Dong Hyeok Lee, Hyery Kim, Sang-Hyun Hwang; Cancer Res Treat. 2025;57(4):1207-1217. Published online January 24, 2025; DOI: https://doi.org/10.4143/crt.2024.1114

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Purpose
Acute myeloid leukemia (AML) shows significant heterogeneity in therapeutic responses. We aimed to develop a gene signature for the stratification of high-risk pediatric AML using publicly available AML datasets, with a focus on literature-based prognostic gene sets.
Materials and Methods
We identified 300 genes from 12 well-validated studies on AML-related gene signatures. Clinical and gene expression data were obtained from three datasets: TCGA-LAML, TARGET-AML, and BeatAML. Least absolute shrinkage and selection operator–Cox regression analysis was used to perform the initial gene selection and to construct a prognostic model using the The Cancer Genome Atlas (TCGA) database (n=132). The final gene signature was validated with two independent cohorts: BeatAML (n=411) and TARGET-AML (n=187).
Results
We identified a six-gene signature (ETFB, ARL6IP5, PTP4A3, CSK, HS3ST3B1, PLA2G4A), referred to as the literature-based signature 6 (LBS6), that was significantly associated with lower overall survival rates across the TCGA (high-risk [HR], 4.2; 95% confidence interval [CI], 2.59 to 6.81; p < 0.001), BeatAML (HR, 1.52; 95% CI, 1.17 to 1.96; p=0.001), and TARGET (HR, 2.05; 95% CI, 1.36 to 3.08; p < 0.001) datasets. The high-LBS6 score group exhibited significantly poorer five-year event-free survival compared to the low-LBS6 score group (HR, 2.09; 95% CI, 1.38 to 3.15; p < 0.001). After adjusting for key risk factors, including gene mutations (WT1, FLT3, and NPM1), protocol-based risk group, white blood cell count, and age, the LBS6 score was independently associated with worse survival rates in validation cohorts.
Conclusion
Our literature-driven approach identified a robust gene signature that stratifies AML patients into distinct risk groups. The LBS6 score shows promise in redefining initial risk stratification and identifying high-risk AML patients.

Citations

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Integrated network propagation identifies prognostic metabolic signatures in acute myeloid leukemia
Jong Keon Song, Hyery Kim, Sang-Hyun Hwang
Journal of Translational Medicine.2025;[Epub] CrossRef

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Lung and Thoracic cancer

Comparison of the Predictive Power of a Combination versus Individual Biomarker Testing in Non–Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors: Hyojin Kim, Hyun Jung Kwon, Eun Sun Kim, Soohyeon Kwon, Kyoung Jin Suh, Se Hyun Kim, Yu Jung Kim, Jong Seok Lee, Jin-Haeng Chung; Cancer Res Treat. 2022;54(2):424-433. Published online July 7, 2021; DOI: https://doi.org/10.4143/crt.2021.583

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Purpose
Since tumor mutational burden (TMB) and gene expression profiling (GEP) have complementary effects, they may have improved predictive power when used in combination. Here, we investigated the ability of TMB and GEP to predict the immunotherapy response in patients with non–small cell lung cancer (NSCLC) and assessed if this combination can improve predictive power compared to that when used individually.
Materials and Methods
This retrospective cohort study included 30 patients with NSCLC who received immune checkpoint inhibitors (ICI) therapy at the Seoul National University Bundang Hospital. programmed cell death-ligand-1 (PD-L1) protein expression was assessed using immunohistochemistry, and TMB was measured by targeted deep sequencing. Gene expression was determined using NanoString nCounter analysis for the PanCancer IO360 panel, and enrichment analysis were performed.
Results
Eleven patients (36.7%) showed a durable clinical benefit (DCB), whereas 19 (63.3%) showed no durable benefit (NDB). TMB and enrichment scores (ES) showed significant differences between the DCB and NDB groups (p=0.044 and p=0.017, respectively); however, no significant correlations were observed among TMB, ES, and PD-L1. ES was the best single biomarker for predicting DCB (area under the curve [AUC], 0.794), followed by TMB (AUC, 0.679) and PD-L1 (AUC, 0.622). TMB and ES showed the highest AUC (0.837) among other combinations (AUC [TMB and PD-L1], 0.777; AUC [PD-L1 and ES], 0.763) and was similar to that of all biomarkers used together (0.832).
Conclusion
The combination of TMB and ES may be an effective predictive tool to identify patients with NSCLC patients who would possibly benefit from ICI therapies.

Citations

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Molecular characteristics, clonal relatedness and surgical outcomes of pulmonary mixed invasive mucinous and non-mucinous adenocarcinoma: a retrospective cohort study
Xinyi Shi, Yang Wang, Nan Yao, Bowen Xue, Lei Guo, Liming Xu, Changbin Zhu, Guiping Qin, Jianming Ying, Yutao Liu, Weihua Li
Molecular Biomedicine.2026;[Epub] CrossRef
Microdroplet-enhanced chip platform for high-throughput immunotherapy marker screening from extracellular vesicle RNAs and membrane proteins
Chuanhao Tang, Zaizai Dong, Shi Yan, Bing Liu, Zhiying Wang, Long Cheng, Feng Liu, Hong Sun, Yimeng Du, Lu Pan, Yuhao Zhou, Zhiyuan Jin, Libo Zhao, Nan Wu, Lingqian Chang, Xiaojie Xu
Biosensors and Bioelectronics.2025; 267: 116748. CrossRef
Visualising the Truth: A Composite Evaluation Framework for Score-Based Predictive Model Selection
Uraquitan Lima Filho, Tiago Alexandre Pais, Ricardo Jorge Pais
BioMedInformatics.2025; 5(3): 55. CrossRef
The DDR-immune fitness score: a biomarker for guiding parp and immunotherapy synergy in extensive-stage small cell lung cancer
Yinxu Zhang, Xiaoyang Chen, Dai Wang, Xuan Zhou, Yuxi Wang, Guangyu Zhang, Xiaomei Liu
Frontiers in Oncology.2025;[Epub] CrossRef
Characterization of Ferroptosis-Associated Subtypes in Psoriasis and the Potential of CHAC1 as a Diagnostic Biomarker Based on Machine Learning
Junming Chen, Jiayi Zhan, Ying Zhou
Clinical, Cosmetic and Investigational Dermatology.2025; Volume 18: 3277. CrossRef
Exploring the ferroptosis-related gene lipocalin 2 as a potential biomarker for sepsis-induced acute respiratory distress syndrome based on machine learning
Jiayi Zhan, Junming Chen, Liyan Deng, Yining Lu, Lianxiang Luo
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease.2024; 1870(4): 167101. CrossRef
Evaluation of Blood Tumor Mutation Burden for the Efficacy of Second-Line Atezolizumab Treatment in Non-Small Cell Lung Cancer: BUDDY Trial
Cheol-Kyu Park, Ha Ra Jun, Hyung-Joo Oh, Ji-Young Lee, Hyun-Ju Cho, Young-Chul Kim, Jeong Eun Lee, Seong Hoon Yoon, Chang Min Choi, Jae Cheol Lee, Sung Yong Lee, Shin Yup Lee, Sung-Min Chun, In-Jae Oh
Cells.2023; 12(9): 1246. CrossRef
Unveiling the role of regulatory T cells in the tumor microenvironment of pancreatic cancer through single-cell transcriptomics and in vitro experiments
Wei Xu, Wenjia Zhang, Dongxu Zhao, Qi Wang, Man Zhang, Qiang Li, Wenxin Zhu, Chunfang Xu
Frontiers in Immunology.2023;[Epub] CrossRef
Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics
Uraquitan Lima Filho, Tiago Alexandre Pais, Ricardo Jorge Pais
BioMedInformatics.2023; 3(4): 1071. CrossRef
Current state and challenges of emerging biomarkers for immunotherapy in hepatocellular carcinoma (Review)
Mo Cheng, Xiufeng Zheng, Jing Wei, Ming Liu
Experimental and Therapeutic Medicine.2023;[Epub] CrossRef

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Review Article

Systems Biology Approaches to Decoding the Genome of Liver Cancer: Ju-Seog Lee, Ji Hoon Kim, Yun-Yong Park, Gordon B. Mills; Cancer Res Treat. 2011;43(4):205-211. Published online December 27, 2011; DOI: https://doi.org/10.4143/crt.2011.43.4.205

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Molecular classification of cancers has been significantly improved patient outcomes through the implementation of treatment protocols tailored to the abnormalities present in each patient's cancer cells. Breast cancer represents the poster child with marked improvements in outcome occurring due to the implementation of targeted therapies for estrogen receptor or human epidermal growth factor receptor-2 positive breast cancers. Important subtypes with characteristic molecular features as potential therapeutic targets are likely to exist for all tumor lineages including hepatocellular carcinoma (HCC) but have yet to be discovered and validated as targets. Because each tumor accumulates hundreds or thousands of genomic and epigenetic alterations of critical genes, it is challenging to identify and validate candidate tumor aberrations as therapeutic targets or biomarkers that predict prognosis or response to therapy. Therefore, there is an urgent need to devise new experimental and analytical strategies to overcome this problem. Systems biology approaches integrating multiple data sets and technologies analyzing patient tissues holds great promise for the identification of novel therapeutic targets and linked predictive biomarkers allowing implementation of personalized medicine for HCC patients.

Citations

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Systems Challenges of Hepatic Carcinomas: A Review
Dhatri Madduru, Johny Ijaq, Sujata Dhar, Saumyadip Sarkar, Naresh Poondla, Partha S. Das, Silvia Vasquez, Prashanth Suravajhala
Journal of Clinical and Experimental Hepatology.2019; 9(2): 233. CrossRef
Epigenetic regulation of hepatocellular carcinoma in non-alcoholic fatty liver disease
Yuan Tian, Vincent Wai-Sun Wong, Henry Lik-Yuen Chan, Alfred Sze-Lok Cheng
Seminars in Cancer Biology.2013; 23(6): 471. CrossRef
The Impact of Network Medicine in Gastroenterology and Hepatology
György Baffy
Clinical Gastroenterology and Hepatology.2013; 11(10): 1240. CrossRef

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Erratum

ERRATUM: Correction for Incorrect Citation of Reference and Wording in a Table: Cancer Res Treat. 2011;43(3):204-204. Published online September 30, 2011; DOI: https://doi.org/10.4143/crt.2011.43.3.204

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