Identification of novel lactate metabolism signatures and molecular subtypes for prognosis in hepatocellular carcinoma

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

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.

Systematic Analysis of Disulfidptosis-Related lncRNAs in Hepatocellular Carcinoma with Vascular Invasion Revealed That AC131009.1 Can Promote HCC Invasion and Metastasis through Epithelial-Mesenchymal Transition

Disulfidptosis, a recently identified pathway of cellular demise, served as the focal point of this research, aiming to pinpoint relevant lncRNAs that differentiate between hepatocellular carcinoma (HCC) with and without vascular invasion while also forecasting survival rates and responses to immunotherapy in patients with vascular invasion (VI+). First, we identified 300 DRLRs in the TCGA database. Subsequently, utilizing univariate analysis, LASSO-Cox proportional hazards modeling, and multivariate analytical approaches, we selected three DRLRs (AC009779.2, AC131009.1, and LUCAT1) with the highest prognostic value to construct a prognostic risk model for VI+ HCC patients. Multivariate Cox regression analysis revealed that this model is an independent prognostic factor for predicting overall survival that outperforms traditional clinicopathological factors. Pathway analysis demonstrated the enrichment of tumor and immune-related pathways in the high-risk group. Immune landscape analysis revealed that immune cell infiltration degrees and immune functions had significant differences. Additionally, we identified valuable chemical drugs (AZD4547, BMS-536924, BPD-00008900, dasatinib, and YK-4-279) for high-risk VI+ HCC patients. In-depth bioinformatics analysis was subsequently conducted to assess immune characteristics, drug susceptibility, and potential biological pathways involving the three hub DRLRs. Furthermore, the abnormally elevated transcriptional levels of the three DRLRs in HCC cell lines were validated through qRT-PCR. Functional cell assays revealed that silencing the expression of lncRNA AC131009.1 can inhibit the migratory and invasive capabilities of HCC cells, a finding further corroborated by the chorioallantoic membrane (CAM) assay. Immunohistochemical analysis and hematoxylin-eosin staining (HE) staining provided preliminary evidence that AC131009.1 may promote the invasion and metastasis of HCC cells by inducing epithelial-mesenchymal transition (EMT) in both subcutaneous xenograft models and orthotopic HCC models within nude mice. To summarize, we developed a risk assessment model founded on DRLRs and explored the potential mechanisms by which hub DRLRs promote HCC invasion and metastasis.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Development and Validation of a Novel Nomogram Integrated with Hypoxic and Lactate Metabolic Characteristics for Prognosis Prediction in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence indicates that hypoxia and lactate metabolism play critical roles in tumor progression and therapeutic efficacy. This study aimed to construct a hypoxia- and lactate metabolism-related prognostic model (HLPM) to evaluate survival and treatment responses for HCC patients and develop a nomogram integrated with HLPM and clinical characteristics for prognosis prediction in HCC.

Methods

Expression profile and clinical data of HCC were obtained from TCGA and ICGC databases. The univariate, LASSO and stepwise multivariate Cox analyses were used to identify the hypoxia- and lactate metabolism-related biomarkers, whose expression levels were then validated in 14 pairs tissue samples and single-cell RNA sequencing dataset. Kaplan-Meier survival curves were utilized to assess the prognostic values of biomarkers or models. Analyses of ImmuCellAI, TIDE and drug sensitivity were conducted to evaluate the therapeutic responses of patients. Furthermore, the nomogram integrated with hypoxic and lactate metabolic characteristics was established through univariate and multivariate Cox analyses. ROC curves, C-index, and calibration curves were depicted to evaluate the performance of the nomogram.

Results

Five hypoxia- and lactate metabolism-related biomarkers (KIF20A, IRAK1, ADM, PPARGC1A and EPO) were used to construct HLPM. The expression of five prognostic biomarkers was validated in 14 pairs tissue samples and single-cell RNA sequencing dataset. Analyses of ImmuCellAI, TIDE and drug sensitivity implied that patients with low-risk score were more sensitive to immunotherapy and major chemotherapeutic agents. The nomogram that contained age, histological grade and risk score of HLPM was developed and exhibited a better capacity in prognosis prediction than HLPM only.

Conclusion

A novel nomogram integrated with hypoxic and lactate metabolic characteristics was developed and validated for prognosis prediction in HCC, providing insight into personalized decision-making in clinical management.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

Identification and validation of a cellular senescence-related lncRNA signature for prognostic prediction in patients with multiple myeloma

Multiple myeloma (MM) is the second most common hematologic malignancy, which primarily occurs in the elderly. Cellular senescence is considered to be closely associated with the occurrence and progression of malignant tumors including MM, and lncRNA can mediate the process of cellular senescence by regulating key signaling pathways such as p53/p21 and p16/RB. However, the role of cellular senescence related lncRNAs (CSRLs) in MM development has never been reported. Herein, we identified 11 CSRLs (AC004918.5, AC103858.1, AC245100.4, ACBD3-AS1, AL441992.2, ATP2A1-AS1, CCDC18-AS1, LINC00996, TMEM161B-AS1, RP11-706O15.1, and SMURF2P1) to build the CSRLs risk model, which was confirmed to be highly associated with overall survival (OS) of MM patients. We further demonstrated the strong prognostic value of the risk model in MM patients receiving different regimens, especially for those with three-drug combination of bortezomib, lenalidomide, and dexamethasone (VRd) as first-line therapy. Not only that, our risk model also excels in predicting the OS of MM patients at 1, 2, and 3 years. In order to verify the function of these CSRLs in MM, we selected the lncRNA ATP2A1-AS1 which presented the largest expression difference between high-risk groups and low-risk groups for subsequent analysis and validation. Finally, we found that down-regulation of ATP2A1-AS1 can promote cellular senescence in MM cell lines. In conclusion, the CSRLs risk model established in present study provides a novel and more accurate method for predicting MM patients' prognosis and identifies a new target for MM therapeutic intervention.