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A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method.
Xin, Haibei; Li, Yuanfeng; Wang, Quanlei; Liu, Ren; Zhang, Cunzhen; Zhang, Haidong; Su, Xian; Bai, Bin; Li, Nan; Zhang, Minfeng.
Afiliación
  • Xin H; Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
  • Li Y; Beijing Institute of Radiation Medicine, Beijing, PR China. Electronic address: liyf_snp@163.com.
  • Wang Q; Dongguan Institute of Gallbladder Disease Research, Dongguan Nancheng Hospital, Dongguan, PR China.
  • Liu R; The 902nd Hospital of the PLA, Bengbu, PR China.
  • Zhang C; Department of Hepatic Surgery I (Ward I), The Third Affiliated Hospital of Naval Military Medical University, Shanghai, PR China.
  • Zhang H; Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
  • Su X; Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
  • Bai B; Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
  • Li N; Department of Hepatic Surgery I (Ward I), The Third Affiliated Hospital of Naval Military Medical University, Shanghai, PR China; The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China. Electronic address: liparislisi@aliyun.com.
  • Zhang M; Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China. Electronic address: dr-zmf@hotmail.com.
Comput Biol Med ; 178: 108663, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38905890
ABSTRACT

BACKGROUND:

Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine. MATERIAL AND

METHODS:

We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).

RESULTS:

We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).

CONCLUSION:

The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos de Riesgos Proporcionales / Carcinoma Hepatocelular / Aprendizaje Automático / Neoplasias Hepáticas Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos de Riesgos Proporcionales / Carcinoma Hepatocelular / Aprendizaje Automático / Neoplasias Hepáticas Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos