Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

Author:

Yerukala Sathipati Srinivasulu1ORCID,Aimalla Nikhila2,Tsai Ming-Ju34,Carter Tonia1,Jeong Sohyun34ORCID,Wen Zhi1,Shukla Sanjay K1,Sharma Rohit5,Ho Shinn-Ying678

Affiliation:

1. Center for Precision Medicine Research, Marshfield Clinic Research Institute , Marshfield, WI 54449 , USA

2. Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System , Marshfield, WI 54449 , USA

3. Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life , Boston, MA , USA

4. Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School , Boston, MA , USA

5. Department of Surgical Oncology, Marshfield Clinic Health System , Marshfield, WI 54449 , USA

6. Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University , Hsinchu , Taiwan

7. College of Health Sciences, Kaohsiung Medical University , Kaohsiung , Taiwan

8. Biomedical Engineering, National Yang Ming Chiao Tung University , Hsinchu , Taiwan

Abstract

Abstract Objective Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. Methods The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. Results HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. Conclusions Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Funder

Marshfield Clinic Research Institute

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,General Medicine

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