Human liver cancer cell lines HepG2 and Huh7, and 293T were purchased from Beijing Beina Chunglian Institute of Biotechnology and The Cell Bank of Type Culture Collection of The Chinese Academy of Sciences. The authenticity of the cell lines used in the present study was verified via short tandem repeat profiling. The plasmids used in the experiments [including the CYP17A1 knockout plasmid (cat. no. L25930), pLenti-Control-sgRNA (cat. no. L00011) and lentiviral packaging vector set A (cat. no. L00002S)] were obtained from Beyotime Biotechnology. According to the manufacturer, the sgRNA incorporated in the commercial CYP17A1 knockout plasmid (cat. no. L25930) was generated using Beyotime's CRISPR/Cas9 sgRNA rapid screening and validation system, and its efficacy had been pre-validated by a T7 endonuclease I (T7EI) assay. The primary reagents included DMEM containing 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.), 0.25% trypsin-EDTA digest (Beijing Solarbio Science & Technology Co., Ltd.), penicillin-streptomycin solution (100X; Beyotime Biotechnology), BCA Protein Quantification Kit (cat. no. 500T; Beyotime Biotechnology) and puromycin (cat. no. ST551; Beyotime Biotechnology). Cell culture conditions were maintained at 37˚C in a humidified incubator with 5% CO₂.

The mRNA expression profiling data of HCC and corresponding paracancerous tissues were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/; accession nos. GSE19665, GSE54236, GSE84402 and GSE121248), which included samples of both sexes and were generated in the original studies by Deng et al (11), Villa et al (12), Wang et al (13) and Wang et al (14). Batch effects were corrected using Surrogate Variable Analysis [sva package, v3.54.0(15)] in R (v4.4.2; R Foundation for Statistical Computing), and the expression matrix of HCC vs. normal tissue for sex groupings was extracted. Differentially expressed genes (DEGs) were screened using limma (v3.62.2) based on |log2 fold change (FC)|≥0.5 and an adjusted P<0.05 after Benjamini-Hochberg correction.

A WGCN was constructed based on the expression matrix of DEGs, and a dynamic tree-cutting algorithm was used to divide the gene modules and screen for those significantly associated with the HCC phenotype (P<0.05 for module significance). The ‘WGCNA’ R package (v1.72) was utilized to construct the network. The network parameters were determined by setting a soft threshold of β=6 (scale-free topological fit index R²>0.85). Modules significantly associated with HCC phenotypes were screened (Pearson correlation; P<0.05) and the top 10% of genes with high intra-module connectivity were extracted as candidate hub genes.

The WGCNA hub genes were intersected with sex-specific DEGs (yielding a total of 36 candidate genes). Given the relatively limited sample size and the small, highly pre-filtered feature set (only 36 candidate genes), further dividing the dataset into separate training and testing cohorts would severely compromise the training process and the statistical power. Instead, to maximize data utility while strictly preventing overfitting, the present study relied entirely on a rigorous 10-fold cross-validation strategy across the entire dataset. A total of four machine learning algorithms, RF, SVM, GLM and XGBoost, were used to assess gene importance. During the 10-fold cross-validation process, the data were iteratively partitioned into training and validation folds, ensuring that the predictive performance of each model was robustly assessed on unseen data within each fold. The predictive performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), with all models achieving an AUC >0.95. The overlapping genes (CYP17A1 and IRX3) ranked in the top ten for importance across all four algorithms were ultimately selected as the core genes. Their sex-specific differential expression profiles were further validated using the GEO database.

For pan-cancer expression analysis, the TIMER2.0 platform (http://timer.cistrome.org/) was used to analyze the expression differences of the core genes across 30 tumors in The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/; Wilcoxon rank-sum test; with P<0.05 as the criterion for determining statistically significant differences). For survival and clinical correlation analyses, TCGA clinical data were integrated and the association between core gene expression and patients' overall survival was evaluated using the Kaplan-Meier method (R package ‘survival’; v3.3.1). Correlations with tumor grade and stage were analyzed using the Spearman rank correlation test. Gene set enrichment analysis was performed to identify the biological pathways associated with the core genes. For immune infiltration analysis, the CIBERSORT algorithm (R package ‘CIBERSORT’; v1.06) was applied to quantitatively analyze the infiltration levels of 22 immune cells and explore the correlation between core gene expression and the immune microenvironment (P<0.05).

Tumors and corresponding paracancerous tissues of liver cancer patients (n=18; 12 men and 6 women; age range, 40-75 years) were included in the present study. The samples were collected between January 2019 and June 2023. Specifically, samples dating from January 2019 to November 2022 were retrospectively accessed from historical archival formalin-fixed paraffin-embedded blocks, while samples from November 2022 to June 2023 were prospectively collected. Based on the study design, the precise inclusion criteria were: i) Patients who underwent radical resection for liver cancer; ii) pathologically confirmed primary liver cancer; iii) availability of matched paraffin-embedded tumor and paracancerous tissues (with paracancerous tissue strictly defined as being >10 mm away from the tumor margin). The exclusion criteria were: i) Patients who received preoperative radiotherapy, chemotherapy or targeted therapy; ii) presence of other primary malignancies. Following surgical resection, fresh tissues were snap-frozen and stored at -80˚C within 30 min, while matched tissue blocks were routinely formalin-fixed and paraffin-embedded for subsequent analysis. The present study was conducted with the approval of the Ethics Committee of the First Affiliated Hospital of Dali University (Approval no. OFY20221122001), which explicitly covered both the retrospective use of archived material and the prospective collection of new samples. Written informed consent was obtained from all patients prior to their participation; notably, for the newer prospectively collected samples, dedicated written informed consent for research use was strictly obtained prior to surgery. Strict confidentiality of patient identities was maintained. Detailed clinicopathological characteristics, including age, biological sex, tumor stage and viral hepatitis status, were retrospectively collected from medical records.

Paraffin sections were deparaffinized with xylene and rehydrated with descending gradient ethanol. Antigen retrieval was performed in sodium citrate buffer (pH 6.0) at 100˚C for 20 min. After blocking with goat serum blocking solution (cat. no. C0265; Beyotime Biotechnology) at room temperature for 10 min, the sections were incubated in a humidified chamber with rabbit anti-CYP17A1 primary antibody (1:200; cat. no. bsm-54306R; Bioss) overnight at 4˚C, followed by an HRP-labeled goat anti-rabbit secondary antibody (1:1,000; cat. no. G1213-100UL; Wuhan Servicebio Technology) at 37˚C for 30 min. Visualization was achieved using DAB, followed by hematoxylin counterstaining at room temperature for 15 sec. Staining intensity (0-3) and the percentage score of positive cells (1-4) were independently evaluated by two pathologists in a double-blind manner; the final IHC score was calculated as intensity x percentage score, and discordant cases were reviewed jointly to reach consensus.

To construct stable CYP17A1 knockout cells, 293T cells were transfected at 37˚C using a third-generation lentiviral packaging system consisting of the CYP17A1 knockout plasmid (cat. no. L25930; Beyotime Biotechnology), pMDLg, Rev and VSV-g plasmids. For each 10-cm dish, 10 µg transfer plasmid, 6.5 µg pMDLg, 2.5 µg Rev and 3.5 µg VSV-g were mixed in 500 µl serum- and antibiotic-free DMEM and transfected using 40 µl Lipo6000 (cat. no. C0526; Beyotime Biotechnology) at 37˚C for 6 h. The medium was replaced after 4-6 h, and viral supernatants were collected at 24 and 48 h, filtered through a 0.45-µm membrane, and used to infect HepG2 cells. Because a fixed MOI was not determined, transduction conditions were optimized empirically using 0.5-2.0 ml viral supernatant/well according to transduction efficiency; 1 ml viral supernatant plus Polybrene (8 µg/ml; cat. no. ST1380; Beyotime Biotechnology) was used for routine infection in 6-well plates. Cells were transduced for 48 h, after which puromycin selection was initiated at 2 µg/ml and maintained at the same concentration for >1 week until stable knockout cells were obtained. The CYP17A1 sgRNA sequence was 5'-GCACCAGGGCACCTTCTCTT-3', and an empty vector was used as the negative control (NC).

Total RNA was extracted from cells using TRIzol^® reagent (cat. no. 15596026; Invitrogen; Thermo Fisher Scientific, Inc.). Reverse transcription was performed using HiScript III All-in-one RT SuperMix Perfect for qPCR (cat. no. R333-01; Vazyme Biotech Co, Ltd.) according to the manufacturer's instructions (37˚C for 15 min and 85˚C for 5 sec). qPCR was performed using ChamQ Universal SYBR qPCR Master Mix (cat. no. Q311-02; Vazyme Biotech Co, Ltd.) on cDNA derived from the indicated cells, and relative mRNA expression was calculated using the 2^-ΔΔCq method (16) with GAPDH as the internal reference. The thermocycling conditions were 95˚C for 30 sec, followed by 40 cycles of 95˚C for 10 sec and 60˚C for 30 sec. The forward and reverse sequences of all primers used were as follows: CYP17A1 Forward: 5'-AGAAGTTATCATCAATCTGTGGGC-3', Reverse: 5'-CTGCTCCGAAGGGCAAATA-3'; GAPDH Forward: 5'-GGAAGCTTGTCATCAATGGAAATC-3', Reverse: 5'-TGATGACCCTTTTGGCTCCC-3'.

Total protein was extracted from cells using RIPA lysis buffer (cat. no. R0010; Beijing Solarbio Science & Technology) supplemented with PMSF. Protein concentrations were determined using a BCA Protein Assay kit. Equal amounts of protein (20 µg/lane) were separated by 10% SDS-PAGE and transferred onto PVDF membranes (cat. no. IPVH00010; MilliporeSigma) at 200 mA for 90 min. The membranes were blocked with 5% non-fat milk in TBST (0.1% Tween-20) at room temperature for 2 h and incubated with rabbit anti-CYP17A1 antibody (1:1,000; cat. no. bsm-54306R; Bioss) and mouse anti-GAPDH antibody (1:1,000; cat. no. GB12002-100; Wuhan Servicebio Technology) overnight at 4˚C, followed by HRP-conjugated goat anti-rabbit IgG (1:5,000; cat. no. bs-0295G-HRP; Bioss) or HRP-conjugated goat anti-mouse IgG (1:5,000; cat. no. bs-0296G-HRP; Bioss) for 1 h at room temperature. Signals were visualized using BeyoECL Plus chemiluminescence reagent (cat. no. P0018S; Beyotime Biotechnology).

Cell Counting Kit-8 (CCK-8) was used to measure cell proliferation (Dojindo Laboratories, Inc.). Cells were seeded in 96-well plates (5x10³ cells/well; six replicates per group) and cell viability was assessed at 0, 24, 48 and 72 h by measuring the OD450. In the functional cell-based assays, the KO-CYP17A1-HepG2 cell line was used as the experimental group, and the KO-Control-HepG2 cell line was used as the control group, to explore the effects of CYP17A1 knockout on the proliferation, migration and invasion ability of the cells.

Cell migration was evaluated using a scratch wound-healing assay. For CYP17A1 knockout experiments, HepG2 NC and KO cells were used; for Saikosaponin A experiments, HepG2 and Huh7 cells were treated with 20 µM Saikosaponin A (Aladdin Biochemical Technology Co., Ltd.). Cells were seeded in 6-well plates and allowed to reach ~100% confluence, scratched with a 200-µl pipette tip, washed three times with PBS, and then cultured in complete medium. Images were captured at 0 and 24 h under an inverted light microscope (magnification, x100), and wound-healing area was quantified using ImageJ (v1.53; National Institutes of Health). For invasion assays, Matrigel (cat. no. M8370; Beijing Solarbio Science & Technology Co., Ltd.) was diluted to ~200 µg/ml, 100 µl was added to each upper chamber and allowed to polymerize at 37˚C for 4 h. Transwell inserts (8-µm pore size; cat. no. WG3422; Wuhan Servicebio Technology) were used. Cells were resuspended at 5x10⁵ cells/ml in serum-free DMEM, and 200 µl cell suspension was added to the upper chamber, while 600 µl complete medium containing 10% FBS was added to the lower chamber. After incubation for 24 h at 37˚C, cells on the lower membrane surface were fixed with 4% paraformaldehyde for 20 min at room temperature, stained with 0.1% crystal violet for 10 min at room temperature, and counted in five random fields under an inverted light microscope (magnification, x200).

The crystal structure of the CYP17A1 protein was retrieved from the PDB database (https://www.rcsb.org/). Water molecules and co-crystallized ligands were removed using PyMOL (v2.5.2; https://pymol.org/2/). A Traditional Chinese Medicine monomer library containing 4,080 compounds was obtained from TargetMol (https://www.targetmol.com/compound-library/traditional-chinese-medicine-monomers-library.html) and virtually screened using AutoDock Vina (v1.2.3). For compounds demonstrating binding energies ≤-10 kcal/mol, molecular docking interactions with the CYP17A1 protein were further analyzed using PyMOL and PLIP software (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index). Saikosaponin A (Aladdin Biochemical Technology Co., Ltd.) exhibited the highest binding affinity for CYP17A1 and was therefore selected for subsequent in vitro validation. The concentration gradient of Saikosaponin A was 0-30 µM. In each experiment, the changes in cell proliferation, migration and invasion ability of HepG2 and Huh7 cells were observed under different concentrations of Saikosaponin A, and the dose-dependent inhibitory effects were analyzed.

Experimental data were analyzed using R (v4.4.2; R Foundation for Statistical Computing), GraphPad Prism (v6.0; Dotmatics) and ImageJ (v1.53; National Institutes of Health). Data are presented as mean ± SD. Two-group comparisons were performed using unpaired, two-tailed Student's t-tests. For >2 groups, one-way ANOVA followed by Tukey's post hoc test was used. Time-course proliferation data (CCK-8) were analyzed using two-way ANOVA with Bonferroni post hoc correction. Differential expression analyses were performed using limma (v3.62.2) with Benjamini-Hochberg false discovery rate correction. Pearson correlation analysis was used for WGCNA module-trait associations, Wilcoxon rank-sum tests were used for pan-cancer expression comparisons, and Spearman rank tests were used for correlations with tumor grade and stage. Survival analyses were performed using Kaplan-Meier curves and two-sided log-rank tests. Because late-stage crossover/convergence was observed in Fig. 3C, sensitivity analyses were additionally conducted using i) administrative truncation at 7 years, ii) weighted Gehan-Breslow testing (rho=1) and iii) a two-stage test [TSHRC package (v0.1-6)]. P<0.05 was considered statistically significant.

A total of 36 candidate genes were screened from samples from male and female patients with HCC by differential expression analysis combined with WGCNA (Fig. 1A and B). The evaluation results showed that the AUC of all models was >0.95 (Fig. 1C) and the residual analysis showed that the model residual values were <0.25 (Fig. 1D), which suggested that the constructed models achieved high discriminatory ability within the cross-validation framework. An intersectional analysis of the top ten genes ranked in importance by the four algorithms identified CYP17A1 and IRX3 as the core genes differentially expressed in HCC samples from men (Fig. 1E).

To verify the differences in the expression of CYP17A1 and IRX3 in HCC tissues of different sexes, their mRNA expression levels were further evaluated using the comprehensive GEO datasets. The results showed that CYP17A1 and IRX3 mRNA expression levels in HCC tissues from men were significantly higher when compared with those in paired paracancerous tissues (P<0.001, Fig. 2A and C). However, there was no significant difference in CYP17A1 and IRX3 mRNA expression between HCC tissues from women and their paired paracancerous tissues (P>0.05; Fig. 2B and D). Subgroup analysis further strictly compared tumor tissues to their sex-matched normal controls. The results showed that the expression levels of CYP17A1 and IRX3 were elevated in the male patient HCC group compared with the normal male group (both P<0.001, Fig. 2E and F). These data verified that CYP17A1 and IRX3 were differentially expressed only in male patient HCC, suggesting a sex-specific regulatory pattern. Notably, while CYP17A1 was subjected to further experimental validation in the present study, IRX3 lacks functional validation and currently serves as a hypothesis-generating candidate gene warranting future investigation.

To explore the broader context of CYP17A1, a pan-cancer expression analysis was performed. It showed that CYP17A1 was generally lowly expressed in the majority of tumors, such as cholangiocarcinoma and renal cancer, but exhibited specific high expression in HCC, rectal adenocarcinoma and gastric cancer (P<0.001; Fig. 3A). In a subsequent, strictly independent HCC-specific validation using the TCGA-LIHC dataset, the expression of CYP17A1 in HCC tissues was significantly increased compared with that in normal tissues (P<0.001; Fig. 3B). The survival analysis with the standard log-rank test indicated no statistically significant association with overall survival (log-rank P>0.05; Fig. 3C). Considering the late-stage curve crossover, additional sensitivity analyses were performed: A 7-year truncated log-rank (P>0.05), weighted Gehan-Breslow (P<0.05) and two-stage test (P=>0.05), indicating a trend-level but non-robust prognostic association. Furthermore, the correlation analysis of CYP17A1 expression and immune cell infiltration showed a positive correlation with M1 macrophage infiltration (R=0.33; P<0.05; Fig. 3D) and a negative correlation with activated dendritic cell infiltration (R=-0.3; P<0.05; Fig. 3E).

Validation of CYP17A1 protein expression by IHC suggested that CYP17A1 protein was significantly higher in male patient HCC tissues when compared with that in paracancerous tissues (P<0.001; Fig. 4A and B). By contrast, there was no significant difference in female patient samples (P>0.05; Fig. 4C and D). This protein-level result was consistent with the mRNA expression patterns. To investigate the functional mechanisms of CYP17A1 in vitro, stable knockout of CYP17A1 was established using lentivirus specifically in the HepG2 cell line. The mRNA (Fig. 5A) and protein (Fig. 5B and C) expression levels of HepG2 cells were significantly reduced following CYP17A1 knockout (P<0.001). Functional cell-based assays demonstrated that knockout of CYP17A1 inhibited HepG2 cell proliferation at 72 h (P<0.001; Fig. 5D; Table SI), reduced migration capacity by 78% in the scratch healing assay (P<0.001; Fig. 5E and F) and attenuated invasive capacity by 65% in the Transwell assay (P<0.001; Fig. 5G and H). These results suggest that CYP17A1 potentially contributes to the malignant phenotypes of HCC cells in vitro.

In a virtual screening of 4,080 herbal monomers using the CYP17A1 protein as a receptor, molecular docking predicted that Saikosaponin A possessed the highest binding affinity for the CYP17A1 protein, with a binding energy of -12.5 kcal/mol and the formation of 6 pairs of hydrogen bonds (Table I; Fig. 6A). Based on these predictive docking data, Saikosaponin A was selected as a putative compound targeting CYP17A1 for further cellular validation. In vitro experiments evaluated the effects of Saikosaponin A on HCC cell phenotypes. Cell proliferation assays demonstrated that treatment with different concentrations of Saikosaponin A significantly inhibited the proliferation of HepG2 and Huh7 cells in a dose- and time-dependent manner (Fig. 6B and C; Tables SII and SIII). Furthermore, after treatment with 20 µM of Saikosaponin A for 24 h, the scratch healing rates of HepG2 and Huh7 cells were reduced by 63 and 58%, respectively (P<0.001; Fig. 6D-G). In response to the image-quality query, representative wound-healing control images were replaced with fields showing continuous near-confluent monolayers outside the wound area, and quantification was recalculated from qualified fields (Fig. 6F). Similarly, Transwell assays revealed that the number of invading HepG2 and Huh7 cells was reduced by 52 and 48%, respectively (P<0.01; Fig. 6H-J). These inhibitory effects on migration and invasion were also dose-dependent. This suggests that Saikosaponin A exhibits a significant inhibitory effect on the malignant phenotypes of HCC cells, highlighting it as a potential therapeutic candidate, although direct CYP17A1-dependency requires further rescue validations.