Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma

Table 1 Representative targeted drugs and signaling pathways in liver cancer

Representative targeted drugs	Related signaling pathways
Sorafenib	Ras/Raf/MEK/ERK signaling pathway
Apatinib, lenvatinib, bevacizumab, ramelimumab	VEGF signaling pathway
Erlotinib, cetuximab	EGF signaling pathway
Cabozantinib	HGF/c-Met signaling pathway

Raf: Rapidly accelerated fibrosarcoma; MEK: Mitogen-activated protein kinase kinase; ERK: Extracellular signal-regulated kinase; EGF: Epidermal growth factor; VEGF: Vascular endothelial growth factor; HGF: Hepatocyte growth factor; c-Met: Mesenchymal-epithelial transition factor.

Table 2 Enzymes involved in the process of hepatocellular carcinoma and their role in ferroptosis

Related enzyme	Relevant impacts	Ferroptosis
PGAM1	Energy stress/ROS-dependent AKT/LCN2 pathway	Inhibitory
NNMT	MNA/ROS axis	Inhibitory
FMO3	FMO3/TMAO/STAT3/SLC7A11/GPX4 axis	Promotion
KDM4C	MDM2/P53/SLC7A11/GPX4 axis	Inhibitory
DHCR7	TMEM147/STAT2/DHCR7/27HC axis	Inhibitory
USP8	USP8/OGT/SLC7A11 signaling pathway, Wnt/β-catenin/GPX4 axis	Inhibitory
PRMT9	HBx/PRMT9/HSPA8/CD44 axis	Inhibitory
HMOX1	Nrf2/HO-1/GPX4 axis	Promotion
ENO1	ENO1-IRP1-Mfrn1 pass-through	Inhibitory
FASN	FASN/HIF1α/SLC7A11 pass-through	Promotion
APE1	AKT/GSK3β/Nrf2/SLC7A11/GPX4 axis	Inhibitory
GSTZ1	Keap1/Nrf2/GPX4 pathway	Promotion
PSTK	TrxR/Trx/GSH/GPX4 pathway	Inhibitory
QSOX1	EGFR/Nrf2/GPX4 pathway	Promotion
RNA helicase DDX5	Wnt/β-catenin/GPX4 signaling pathway	Promotion
DUSP4	YTHDC1/FTL/FTH1/Lipid ROS pathway	Inhibitory
MerTK	ERK/SP1/SLC7A11 pathway	Inhibitory
UCHL3	Ub-proteasome/Wnt/β-catenin/GPX4 pathway	Inhibitory
AKR1C3	Hippo/YAP/TAZ/SLC7A11 pathway	Inhibitory
Deubiquitinating enzyme (EIF3H)	OGT/Lipid ROS/GSH pathway	Inhibitory
USP24	K48/Beclin1/FPN pathway	Promotion
LHPP	LHPP/P13K/AKT pathway	Promotion

PGAM1: Phosphoglycerate translocase 1; ROS: Reactive oxygen species; AKT: Protein kinase B; LCN2: Lipid carrier protein 2; NNMT: Nicotinamide transmethylase; MNA: Multifunctional microneedle array; FMO3: Flavin monooxygenase 3; TMAO: Trimethylamine N-oxide; STAT3: Signal transducer and activator of transcription 3; GPX4: Glutathione peroxidase 4; KDM4C: Lysine specific demethylase 4; MDM2: Murine double minute 2; DHCR7: 7-dehydrocholesterol reductase; USP8: Ubiquitin specific peptidase 8; OGT: O-GlcNAc transferase; PRMT9: Arginine methyltransferase 9; HSPA8: Heat shock protein A8; HMOX1: Hemoglobin oxygenase 1; Nrf2: Nuclear factor erythroid 2-related factor 2; HO-1: Heme oxygenase 1; ENO1: Α-enolase 1; IRP1: Iron regulatory protein 1; FASN: Fatty acid synthase; HIF1α: Hypoxia-inducible factor 1-alpha; APE1: Apurinic/apyrimidinic endonuclease 1; GSK3β: Glycogen synthase kinase-3β; GSTZ1: Glutathione S-transferase zeta 1; Keap1: Kelch-like ECH-associated protein 1; PSTK: Phosphoseryl-tRNA kinase; TrxR: Thioredoxin reductase; Trx: Thioredoxin; GSH: Glutathione; QSOX1: Quiescin sulfhydryl oxidase 1; EGFR: Epidermal growth factor receptor; FTH1: Ferritin heavy chain 1; DUSP4: Dual-specific phosphatase 4; MerTK: MER proto-oncogene tyrosine kinase; ERK: Extracellular signal-regulated kinase; UCHL3: Ubiquitin C terminal hydrolase L3; AKR1C3: Aldehyde-keto reductase 1C3; USP24: Ubiquitin-specific proteases 24; LHPP: Lysine phosphatase; P13K: Phosphatidylinositol-3-kinase.

Table 3 A review related to artificial intelligence prediction models for liver cancer

Ref.	Model name	Model type	Aim	Dataset	Model checking	Major limitation
[88]	Hybrid model	Logistic regression	MVI	Total: 773. Training set: 334. Internal test set: 142. External test set: 141	Internal test set: AUC = 0.86. External test set: AUC = 0.84	Selection bias; most enrolled patients had a virus-related HCC
[89]	DECT	Logistic regression	MTM	Total: 262. Training set: 146. Internal test set: 35. External test set: 81	Internal test set: AUC = 0.87. External test set: AUC = 0.89	Different centers; overfitting
[90]	ABRS-P	CLAM	Biomarker of sensitivity to atezolizumab-bevacizumab	Total: 122. ABRS-P-high: 74. ABRS-P-low: 48		Retrospective
[91]	AI-based pathology models	CLAM	Predict the activation of 6 immune gene	Total: 336	AUC: 0.78-0.91	Needs further validation with clinical data
[92]	PLAN-B-DF	GBM	HCC prediction	Training set: 4188. Internal test set: 1397. External test set: 2883	CI: 0.91	Impose radiation exposure; generalizability limited
[93]	AI prediction model for liver cancer recurrence	MLP	Evaluate the survival of patients with HCC	Total: 912	AUC: 0.862	Pack of longitudinal data
[94]	UBE2S related model	MLP	Evaluate the survival of patients with HCC	Total: 370. Training set: 224. Test set: 146	The 1-, 2-, 3-year survival AUC values were 076, 0.72, 0.68
[95]	PAGE-B	Cox regression	Risk prediction tool	Total: 2963	CI: 0.77	The lack of systematic screening information on HDV coinfection
[96]	SCHMOWDER	SCHMOWDER	Predicting patient survival after HCC recurrence and surgery	The transplant cohort: 300. The resection cohort: 169	The transplant cohort: CI = 0.83 (RFS); CI = 0.87 (DSS). The resection cohort: CI = 0.64 (RFS); CI = 0.77 (DSS)
[97]	MoRAL-AI	DNN	Recurrence	Total: 563. Training set: 349. Test set: 214	CI: 0.75	Single-region HBV cohort
[98]	R3-AFP	Logistic	Validation	Total: 508	CI: 0.75	Restricted to SiLVER trial
[99]	Reticulin-CNN	CNN	Prognosis	Total: 105	CI > 0.7	Small sample; missing HBV/HCV data
[100]	IR-lncRNA	Cox/LASSO	Recurrence	Total: 319. Training set: 161. Test set: 158	CI: 0.732	Needs external validation
[101]	CT-DCNN	DCNN	Diagnosis	Training set: 7512. Internal test set: 385. External test set: 556	The internal test set: AUC = 0.887. The external test set: AUC = 0.883	Central China predominance
[102]	TIL score	Cox	Quantify	Training set: 124. Test set 1: 82. Test set 2: 54	Training set: CI = 0.770. Test set 1: CI = 0.769. Test set 2: CI = 0.712	Slide alignment and selection bias
[103]	DSFR	Logistic regression	Predict early recurrence	Total: 208. Training set: 180. Test set: 28	Training set: AUC = 0.782. Test set: AUC = 0.744	Small sample
[104]	Aiforia	AI-based histological model	Histological outcome	Total: 101	rs = 0.72
[105]	LDA	Discriminant analysis	MVI	Total: 140. Training set: 98. Test set: 42	Training set: AUC = 0.995. Test set: AUC = 0.913	Small size; 2-center variability
[106]	Random forest model	Random forest model	Waitlist Dropout	Total: 15444	C-statistic: 0.74	Retrospective; not externally validated
[107]	Multiple ML models	ML (DT, SVM, NN, etc.)	Survival	Total: 393	Early-stage: Recall = 91% (6 months). Advanced: Accuracy = 92% (3 years)	Small, single center, retrospective
[108]	CNN	CNN	Predicting the outcome of ICIs treatment	Training set: PD = 197; PR = 271; SD = 342	F1 score 698%
	YOLO	YOLO
[109]	Cox PH model	Cox regression	Risk	Total: 790	AUC = 0.86	Retrospective; small sample
[110]	CART	Decision tree	HCC	Training set: 55	AUC: 0.950	Small sample; no validation
[111]	nVR	Radiomics	Recurrence	Training set: 130. Test set: 57	Training set: AUC = 0.759. Test set: AUC = 0.765	Retrospective; TACE method bias
[112]	3D-ResNet50	Deep learning	Grade	Total: 858. Training set: 524. Validation set: 131. External test set: 65. Internal temporal test set: 138	Training set: AUC = 0.82. Validation set: AUC = 0.825. External test set: AUC = 0.78. Internal temporal test set: AUC = 0.81	Retrospective; single-phase imaging
[113]	DCNN	Deep learning	Prognosis	Total: 236	Training set: CI = 0.735 (RFS); CI = 0.712 (OS). Test set: CI = 0.718 (RFS); CI = 0.740 (OS)	Single-center; small sample
[114]	MAPL-5	ML	HCC	Training set: 1182. External test set: 562	Training set: AUC = 0.784; balanced accuracy = 0.712. External test set: AUC = 0.862; balanced accuracy = 0.771	Needs external validation
[115]	LightGBM	Radiomics	Biomarker	Training set: 424. Test set: 102	Training set: AUC = 0.866. Test set: AUC = 0.824	Small sample; no multicenter
[116]	Random forest	Radiomics	Grade	Training set: 137. External test set: 28	Training set: AUC = 0.80. External test set: AUC = 0.70	Single-vendor MRI; limited external data
[117]	CRNN	Deep learning	Survival	Total: 207	Training set: 0.777. Test set: 0.704	Small sample; excluded non-lung metastases

MVI: Microvascular invasion; AUC: Area under the curve; HCC: Hepatocellular carcinoma; DECT: Dual-energy computed tomography; MTM: Macrotrabecular-massive; ABRS: Atezolizumab-bevacizumab response signature; CLAM: Clustering-constrained attention multiple-instance learning; AI: Artificial intelligence; GBM: Gradient boosting machine; CI: Confidence interval; MLP: Multilayer perceptron; RFS: Relapse-free survival; DSS: Disease-specific survival; DNN: Deep neural network; HBV: Hepatitis B virus; AFP: Alpha-fetoprotein; CNN: Convolutional neural network; HCV: Hepatitis C virus; LASSO: Least absolute shrinkage and selection operator; DCNN: Deep convolutional neural network; TIL: Tumor-infiltrating lymphocyte; DSFR: Deep semantic segmentation feature-based radiomics; rs: R²d; LDA: Linear Discriminant Analysis; ML: Machine learning; DT: Decision tree; SVM: Support vector machine; NN: Neural network; YOLO: You only look once; ICIs: Immune checkpoint inhibitors; PD: Progressive disease; PR: Partial response; SD: Stable disease; CART: Classification and regression trees; TACE: Transarterial chemoembolization; OS: Overall survival; MRI: Magnetic resonance imaging.