Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1

Abstract

Background: Disulfidptosis is a newly discovered programmed cell death pathway that may be connected to tumorigenesis and development, showing promise as a novel treatment strategy for cancer. This study aims to construct a prognostic model of disulfidptosis-related Long non-coding RNAs (DRLRs) within the Asian HCC population and to investigate the impact of DRLRs on HCC.

Methods: Utilising a combination of univariate Cox, Lasso-Cox, and multivariate Cox analyses, five pivotal DRLRs (AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1) were identified, forming a robust prognostic signature. Subsequent validations included Receiver Operating Characteristic (ROC) and Concordance Index analyses, alongside Principal Component Analysis. Comprehensive bioinformatics analysis was performed on the hub DRLRs, followed by experimental validation using quantitative real-time polymerase chain reaction and cellular functional assays.

Results: The risk score independently predicted prognosis, outperforming traditional clinical-pathological factors across varying ages, tumour stages, and pathological classifications in the cohort. A nomogram integrating these variables demonstrated capability in forecasting survival. Multivariate analysis confirmed that the risk score and AJCC TNM staging are independent prognostic factors for predicting overall survival (OS) in Asian HCC patients (both P < 0.001). The prognostic model's ROC area under the ROC values for 1-, 3-, and 5-year predictions were 0.837, 0.794, and 0.783, respectively, indicating its strong diagnostic and prognostic value. Pathway and immune landscape analyses elucidated the biological underpinnings and immune modulations associated with the high-risk group. Immune landscape analysis indicated that both immunescore (P < 0.001) and estimatescore (P < 0.05) were significantly decreased in the high-risk group, with both specific and non-specific immune responses being significantly suppressed, while the tumour immune dysfunction and exclusion score was notably increased (P < 0.001). Tumour mutational burden (TMB) analysis revealed a significantly higher TMB in the high-risk group (P = 0.033) and shorter OS for HCC patients in the high TMB subgroup (P = 0.002). Notably, Potential chemotherapeutic agents (PFI3, 5-Fluorouracil, BPD-00008900, GDC0810, and AZ6102) were identified for high-risk group. Experimental validations through quantitative PCR and in vitro assays confirmed the deregulation of these DRLRs in HCC, with functional studies highlighting the potential of ZNF337-AS1 silencing in curtailing tumour invasiveness.

Conclusion: Our investigations validate a DRLR-based risk scoring model as an effective prognostic tool for Asian HCC. This model not only enhances understanding of disulfidptosis's role in HCC but also facilitates personalised treatment strategies, potentially improving patient outcomes.

Keywords: Bioinformatics; Disulfidptosis; Hepatocellular carcinoma; Long noncoding RNA; ZNF337-AS1.

Fig. 1

Identification of prognostic signatures of Disulfidptosis-related Long non-coding RNAs (DRLRs) in Asian hepatocellular carcinoma (HCC) patients. A Sankey diagram indicating the association between disulfidptosis-related genes and DRLRs. B, C Application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression to the selection of DRLRs. D Forest plot representing results from multivariable Cox regression analysis. E Correlation heatmap of DRLRs within the risk model with genes related to cuproptosis. F Kaplan–Meier (KM) survival curve analysis to discern differences in overall survival (OS) between high- and low-risk groups in the Asian HCC training cohort and internal validation cohort. G Distribution of patient risk scores and overall survival status within the training and internal validation cohorts. H Expression heatmap of the 5 DRLRs in both training and internal validation cohorts

Fig. 2

KM survival curve analyses of the 5-DRLR signature model for Asian HCC patient subgroups based on clinical and pathological features, comparing high- and low-risk groups for OS. A Age < 60 years B Age ≥ 60 years C Female D Male E Grade 1–2 F Grade 3–4 G Stage T1–2 H Stage T3–4 I N0 J M0 K Stage TNM I–II L Stage TNM III–IV M With a family history of tumours N Without a family history of tumours O Alpha-fetoprotein (AFP) ≤ 25 ng/mL P AFP > 25 ng/mL Q Serum albumin ≤ 4.0 g/dL R Serum albumin > 4.0 g/dL S Eastern Cooperative Oncology Group (ECOG) performance score < 1 T ECOG performance score ≥ 1

Fig. 3

Independent prognostic analysis and validation of the 5-DRLR signature model, nomograms and Principal Component Analysis (PCA). A Univariate Cox analysis showing statistical significance of Stage TNM and 5-DRLRs risk scoring model (P < 0.001). B Multivariate Cox analysis indicating statistical significance of Stage TNM and 5-DRLRs risk scoring model (P < 0.001). C KM survival curve for progression-free survival (PFS) in high- and low-risk groups within the training cohort. D Time-dependent Receiver Operating Signature (timeROC) curves predicting 1-, 3-, and 5-year OS for Asian HCC patients. E MultiROC indicating the superior predictive accuracy of the 5-DRLRs risk model over other clinical parameters. F Concordance index (C-index) demonstrating the favourable predictive accuracy of the risk model. G, H Nomograms based on the 5-DRLR signature scoring and calibration curves predicting 1-, 3-, and 5-year overall survival for Asian HCC. PCA analyses between high- and low-risk groups based on I(a) all genes, I(b) disulfidptosis-related genes, I(c) DRLRs, and I(d) the prognostic 5-DRLRs

Fig. 4

Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA). A Bar chart of the top 10 pathways in the biological process (BP), cellular component (CC), and molecular function (MF) obtained from GO analysis. B Circle diagram of GO enrichment analysis. GSEA within the The Cancer Genome Atlas (TCGA) Asian HCC cohort showed significant differential enrichment pathways for the high-risk group. C and low-risk group D against the c2.all.v2022.1.Hs.symbols.gmt gene set, and significant differential enrichment pathways for the high-risk group (E) and the low-risk group (F) against the c5.all.v202 2.1.Hs.symbols.gmt gene set. For the h.all.v2022.1.Hs.symbols.gmt gene set, significant pathways enriched in the high-risk group (G)

Fig. 5

Immune landscape analysis of Asian HCC patients, the relationship between Tumour Mutation Burden (TMB) and risk scores. A Differences in the tumour microenvironment between high and low-risk groups. B Proportion of 22 immune cells in high and low-risk groups calculated using the CIBERSORT algorithm. C Differences in immune function score between the high and low-risk groups. D Analysis of immune escape differences based on Tumour Immune Dysfunction and Exclusion (TIDE) scoring in high and low-risk groups. E, F Waterfall plots of somatic TMB features in high and low-risk groups. G Differences in TMB between the high and low-risk groups. H KM survival curves between high and low TMB groups. I KM survival curves among four groups combined TMB with risk score grouping

Fig. 6

Drug sensitivity of Asian HCC patients in high and low-risk groups based on the 5-DRLRs signature. Displayed are the top five drugs related to sensitivity and resistance. Patients in the high-risk group for Asian HCC are more sensitive to A PFI3, B 5-Fluorouracil, C BPD-00008900, D GDC0810 and E AZ6102 and more resistant to F XAV939, G Afuresertib, H GSK2578215A, I JQ1 and J Obatoclax Mesylate

Fig. 7

Survival analysis of the five hub DRLRs in the model. High expression of A AC099850.3, B ZNF337-AS1, C LINC01138, D AL031985.3 and E AC131009.1 in Asian HCC patients significantly reduced OS compared to those with low expression (all P < 0.001)

Fig. 8

Correlation of the expression of the five hub DRLRs in the Asian HCC prognostic model with clinical-pathological features. The relationship between age (A), sex (B), pathological staging (C), T staging (D), TNM staging (E), family history of tumour (F), AFP (G), serum albumin (H), ECOG score (I), survival status (J) and expression of the five prognostic hub DRLRs in Asian HCC patients from the TCGA database were compared using the Wilcoxon rank-sum test

Fig. 9

Validation of the expression levels of the five prognostic hub DRLRs in clinical samples and HCC cell lines using quantitative real-time polymerase chain reaction (qRT-PCR). Expression levels of A AC099850.3, B ZNF337-AS1, C LINC01138, D AL031985.3 and E AC131009.1 in paired clinical samples from Asian HCC and adjacent tissues. Expression levels of F AC099850.3, G ZNF337-AS1, H LINC01138, I AL031985.3 and J AC131009.1 in HCC cell lines HepG2, Hep3B2.1-7, HCC-LM3 and normal liver cell line L02

Fig. 10

Impact of ZNF337-AS1 on the proliferation, migration and invasion of the HCC cell line HepG2. Knockdown efficiency of ZNF337-AS1 in HCC cell line HepG2 was assessed by qRT-PCR (A). HepG2 cells transfected with si-negative control (NC) and small interfering RNA (siRNA) were subjected to Cell Counting Kit-8 (CCK-8 assay) (B), colony formation assay (C), wound healing assay (D) and Transwell assay (E)