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Article Open Access

Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening

  • Authors:
    • Zhen Wang
    • Jinghua Ning
    • Hongyuan Zhang
    • Qingfen Ruan
    • Yanhong Zhao
    • Run Qu
    • Congcong Lv
    • Yutong Wu
    • Weidong Liu
    • Xiaoyin Yang
    • Ziming Li
    • Yi Liang
    • Yuzhe Zhang
  • View Affiliations / Copyright

    Affiliations: Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Dali University, Dali, Yunnan 671003, P.R. China, Department of Gastroenterology, The First Affiliated Hospital of Dali University, Dali, Yunnan 671003, P.R. China, Princess Margaret Cancer Centre, University Health Network, TMDT‑MaRS Centre, Toronto, Ontario ON M5G 1L7, Canada
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 74
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    Published online on: April 24, 2026
       https://doi.org/10.3892/br.2026.2147
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Abstract

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study leveraged multi‑omics data and machine learning algorithms to identify key genes associated with sex‑specific differences in HCC and to screen for putative candidate compounds, aiming to provide new insights for sex‑specific therapy. The mRNA expression data of male and female patients with HCC and paracancerous tissues were obtained from the GEO and TCGA databases. To mitigate overfitting, data were partitioned into independent training and testing sets. Candidate genes were screened by differential expression analysis and weighted gene co‑expression network analysis. A total of four complementary algorithms, random forest, support vector machines, generalized linear models and extreme gradient boosting were used to identify key genes with high predictive capability. CYP17A1 and IRX3 were identified as the top differentially expressed core genes associated with HCC in men. Pan‑cancer analysis showed that CYP17A1 was lowly expressed in the majority of tumors, but significantly highly expressed in HCC, rectal adenocarcinoma and gastric cancer (P<0.001). Functional cell‑based assays showed that knockout of CYP17A1 inhibited the proliferation, migration and invasion ability of HCC cells (P<0.001). Immunohistochemistry showed that CYP17A1 protein expression was significantly increased in HCC tissues from male patients when compared with that in paracancerous tissues (P<0.001), whereas there was no significant difference in female patient tissues (P>0.05). Notably, while IRX3 was identified computationally, its functional role remains to be experimentally validated. Molecular docking predicted a potential interaction between the natural compound Saikosaponin A and the CYP17A1 protein, and cellular assays revealed that it dose‑dependently inhibits HCC cell malignant phenotypes. The present study suggests that CYP17A1 is associated with sex differences in HCC, potentially via the androgen signaling axis. Furthermore, IRX3 emerges as a novel hypothesis‑generating candidate gene. Finally, the findings of the present study highlight Saikosaponin A as a putative therapeutic candidate for male patients with HCC, warranting further target‑dependency investigations.
View Figures

Figure 1

Pan-cancer analysis of CYP17A1 and
single-gene analysis in HCC. (A) Expression of CYP17A1 in various
tumors. (B) Expression of CYP17A1 in HCC and paracancerous tissues.
(C) Kaplan-Meier survival curve of CYP17A1 high- and low-expression
groups. (D) Correlation of CYP17A1 expression with M1 macrophages.
(E) Correlation of CYP17A1 expression with activated dendritic
cells. HCC, hepatocellular carcinoma; **P<0.01;
***P<0.001.

Figure 2

Screening for genes central to sex
differences in hepatocellular carcinoma incidence by four machine
learning algorithms. (A) Genes that are differentially expressed
only in male or female patients intersect with the hub genes of
WGCNA. (B) A total of four machine learning algorithms score the
importance of 36 candidate core genes. (C) Receiver operating
characteristic curve of four machine learning algorithms on model
prediction accuracy. (D) Residual values of predictive accuracy of
four machine learning algorithms. (E) Venn diagram of the top 10
most important genes for four machine learning algorithms. WGCNA,
weighted gene co-expression network analysis; Diffgene,
differentially expressed genes; RF, random forest; SVM, support
vector machine; XGB, extreme gradient boosting; GLM, generalized
linear model.

Figure 3

Validation of sex-specific expression
of CYP17A1 and IRX3. (A) Expression of CYP17A1 in male patients
with HCC. (B) Expression of CYP17A1 in female patients with HCC.
(C) Expression of IRX3 in male patients with HCC. (D) Expression of
IRX3 in female patients with HCC. (E) Differences in CYP17A1
expression among four groups. (F) Differences in IRX3 expression
among four groups. Con, control; Treat, treated; HCC,
hepatocellular carcinoma; ns, not significant;
*P<0.05; ***P<0.001.

Figure 4

Immunohistochemical validation of
CYP17A1 protein in HCC tissues. (A) Immunohistochemical images from
HCC and paired paracancerous tissues from a male patient. (B)
Difference in CYP17A1 expression between HCC and paired
paracancerous tissues from a male patient. (C) Immunohistochemical
images from HCC and paired paracancerous tissues from a female
patient. (D) Difference in CYP17A1 expression between HCC and
paired paracancerous tissues from a female patient. HCC,
hepatocellular carcinoma; ns, not significant;
***P<0.001.

Figure 5

A preliminary investigation of the
role of CYP17A1 in hepatocellular carcinoma based on CYP17A1
knockout and functional cell-based assays. (A) Relative expression
of CYP17A1 mRNA in the control group and the KO group. (B) CYP17A1
protein expression in the control group and the KO group. (C)
Difference analysis of CYP17A1 relative expression levels in the
control group and the KO group. (D) Proliferation of the control
group and the CYP17A1 gene KO group over 72 h. (E) Effect of
CYP17A1 gene knockout on HepG2 migration ability under the
microscope at 0 and 24 h (magnification, x100). (F) Difference
analysis of scratch healing rates between the control group and the
KO group. (G) Transwell results of the control group and the KO
group under a microscope. (H) Analysis of the difference in the
relative number of invasive cells in the control group and the KO
group. NC, the control group; KO, the knockout group; OD, optical
density; ***P<0.001.

Figure 6

Screening and in vitro
validation of drug candidates targeting CYP17A1. (A) Saikosaponin A
interacts with the CYP17A1 protein. (B) Cell proliferation of HepG2
cells treated with different concentrations of Saikosaponin A for
24, 48 and 72 h. (C) Cell proliferation of Huh7 cells treated with
different concentrations of Saikosaponin A for 24, 48 and 72 h. (D)
Scratch healing of HepG2 control and Saikosaponin A-treated cells
at 0 and 24 h under the microscope (magnification, x100). (E)
Difference analysis of the scratch healing rate between the Control
group and the drug-treated group. (F) Scratch healing of Huh7
Control and Saikosaponin A-treated cells at 0 and 24 h under the
microscope (magnification, x100). (G) Difference analysis of
scratch healing rates between the Control group and the
drug-treated group. (H) Cell invasion of HepG2 and Huh7 cells in
the Control group and the drug-treated group under the microscope.
(I) Difference analysis of the relative cell number of HepG2 cells
in the Control group and the drug-treated group. (J) Difference
analysis of relative cell numbers of Huh7 cells in the Control
group and the drug-treated group. *P<0.05;
**P<0.01; ***P<0.001.
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Copy and paste a formatted citation
Spandidos Publications style
Wang Z, Ning J, Zhang H, Ruan Q, Zhao Y, Qu R, Lv C, Wu Y, Liu W, Yang X, Yang X, et al: Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening. Biomed Rep 24: 74, 2026.
APA
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R. ... Zhang, Y. (2026). Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening. Biomedical Reports, 24, 74. https://doi.org/10.3892/br.2026.2147
MLA
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R., Lv, C., Wu, Y., Liu, W., Yang, X., Li, Z., Liang, Y., Zhang, Y."Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening". Biomedical Reports 24.6 (2026): 74.
Chicago
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R., Lv, C., Wu, Y., Liu, W., Yang, X., Li, Z., Liang, Y., Zhang, Y."Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening". Biomedical Reports 24, no. 6 (2026): 74. https://doi.org/10.3892/br.2026.2147
Copy and paste a formatted citation
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Spandidos Publications style
Wang Z, Ning J, Zhang H, Ruan Q, Zhao Y, Qu R, Lv C, Wu Y, Liu W, Yang X, Yang X, et al: Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening. Biomed Rep 24: 74, 2026.
APA
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R. ... Zhang, Y. (2026). Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening. Biomedical Reports, 24, 74. https://doi.org/10.3892/br.2026.2147
MLA
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R., Lv, C., Wu, Y., Liu, W., Yang, X., Li, Z., Liang, Y., Zhang, Y."Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening". Biomedical Reports 24.6 (2026): 74.
Chicago
Wang, Z., Ning, J., Zhang, H., Ruan, Q., Zhao, Y., Qu, R., Lv, C., Wu, Y., Liu, W., Yang, X., Li, Z., Liang, Y., Zhang, Y."Machine learning‑based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening". Biomedical Reports 24, no. 6 (2026): 74. https://doi.org/10.3892/br.2026.2147
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