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Pourbagheri-Sigaroodi A, Fallah F, Bashash D, Karimi A. Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC). Cell Biochem Funct 2024; 42:e3913. [PMID: 38269520 DOI: 10.1002/cbf.3913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/14/2023] [Accepted: 12/17/2023] [Indexed: 01/26/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.
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Affiliation(s)
- Atieh Pourbagheri-Sigaroodi
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Fallah
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abdollah Karimi
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Tang M, Zhao Y, Zhao J, Wei S, Liu M, Zheng N, Geng D, Han S, Zhang Y, Zhong G, Li S, Zhang X, Wang C, Yan H, Cao X, Li L, Bai X, Ji J, Feng XH, Qin J, Liang T, Zhao B. Liver cancer heterogeneity modeled by in situ genome editing of hepatocytes. SCIENCE ADVANCES 2022; 8:eabn5683. [PMID: 35731873 PMCID: PMC9216519 DOI: 10.1126/sciadv.abn5683] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Mechanistic study and precision treatment of primary liver cancer (PLC) are hindered by marked heterogeneity, which is challenging to recapitulate in any given liver cancer mouse model. Here, we report the generation of 25 mouse models of PLC by in situ genome editing of hepatocytes recapitulating 25 single or combinations of human cancer driver genes. These mouse tumors represent major histopathological types of human PLCs and could be divided into three human-matched molecular subtypes based on transcriptomic and proteomic profiles. Phenotypical characterization identified subtype- or genotype-specific alterations in immune microenvironment, metabolic reprogramming, cell proliferation, and expression of drug targets. Furthermore, single-cell analysis and expression tracing revealed spatial and temporal dynamics in expression of pyruvate kinase M2 (Pkm2). Tumor-specific knockdown of Pkm2 by multiplexed genome editing reversed the Warburg effect and suppressed tumorigenesis in a genotype-specific manner. Our study provides mouse PLC models with defined genetic drivers and characterized phenotypical heterogeneity suitable for mechanistic investigation and preclinical testing.
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Affiliation(s)
- Mei Tang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Jianhui Zhao
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shumei Wei
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Nairen Zheng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Didi Geng
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Shixun Han
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Yuchao Zhang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Guoxuan Zhong
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Shuaifeng Li
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Chenliang Wang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Huan Yan
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiaolei Cao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Li Li
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Xueli Bai
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Junfang Ji
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Xin-Hua Feng
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Tingbo Liang
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Corresponding author. (T.L.); (B.Z.)
| | - Bin Zhao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Shaoxing Institute, Zhejiang University, Shaoxing 321000, China
- Corresponding author. (T.L.); (B.Z.)
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Nacer DF, Liljedahl H, Karlsson A, Lindgren D, Staaf J. Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor. Brief Bioinform 2021; 22:6272790. [PMID: 33971670 PMCID: PMC8574611 DOI: 10.1093/bib/bbab154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 12/24/2022] Open
Abstract
Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.
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