Prognostic pyroptosis-related lncRNA signature predicts the efficacy of immunotherapy in hepatocellular carcinoma

AC099850.3 promotes HBV-HCC cell proliferation and invasion through regulating CD276: a novel strategy for sorafenib and immune checkpoint combination therapy

BACKGROUND

This study investigates the molecular mechanisms of CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and sorafenib (SF) for improving hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

METHODS

A dataset of 44 HBV-HCC patients and their survival information was selected from the TCGA database. Immune genes related to survival status were identified using the ImmPort database and WGCNA analysis. A prognostic risk model was constructed and analyzed using Lasso regression. Differential analysis was performed to screen key genes, and their significance and predictive accuracy for HBV-HCC were validated using Kaplan-Meier survival curves, ROC analysis, CIBERSORT analysis, and correlation analysis. The correlation between AC099850.3 and the gene expression matrix was calculated, followed by GO and KEGG enrichment analysis using AC099850.3 and its co-expressed genes. HepG2.2.15 cells were selected for in vitro validation, and lentivirus interference, cell cycle determination, CCK-8 experiments, colony formation assays, Transwell experiments, scratch experiments, and flow cytometry were performed to investigate the effects of key genes on HepG2.2.15 cells. A subcutaneous transplanted tumor model in mice was constructed to verify the inhibitory effect of key genes on HBV-HCC tumors. Subsequently, pH-triggered drug release NPs (CC@AC&SF@PP) were prepared, and their therapeutic effects on HBV-HCC in situ tumor mice were studied.

RESULTS

A prognostic risk model (AC012313.9, MIR210HG, AC099850.3, AL645933.2, C6orf223, GDF10) was constructed through bioinformatics analysis, showing good sensitivity and specificity in diagnostic prediction. AC099850.3 was identified as a key gene, and enrichment analysis revealed its impact on cell cycle pathways. In vitro cell experiments demonstrated that AC099850.3 promotes HepG2.2.15 cell proliferation and invasion by regulating immune checkpoint CD276 expression and cell cycle progression. In vivo, subcutaneously transplanted tumor experiments showed that AC099850.3 promotes the growth of HBV-HCC tumors in nude mice. Furthermore, pH-triggered drug release NPs (CC@AC&SF@PP) loaded with AC099850.3 siRNA and SF were successfully prepared and delivered to the in situ HBV-HCC, enhancing the effectiveness of combined therapy for HBV-HCC.

CONCLUSIONS

AC099850.3 accelerates the cell cycle progression and promotes the occurrence and development of HBV-HCC by upregulating immune checkpoint CD276 expression. CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and SF improve the effectiveness of combined therapy for HBV-HCC.

Bioinformatics analysis of neutrophil-associated hub genes and ceRNA network construction in septic cardiomyopathy

Septic cardiomyopathy (SCM) is a critical sepsis complication characterized by reversible cardiac depression during early septic shock. Neutrophils, integral to innate immunity, can mediate organ damage when abnormal, but their specific role in sepsis-induced myocardial damage remains elusive. Our study focuses on elucidating the role of Neutrophil-Related Genes (NRGs) in SCM, finding early diagnosis and treatment biomarkers. We identified shared differentially expressed genes (DEGs) from datasets GSE79962 and GSE44363 and pinpointed hub DEGs using the cytoHubba plugin in Cytoscape software. The Neutrophil-Related Hub Gene (NRHG) MRC1 was identified via intersecting hub DEGs with NRGs from WGCNA. We validated MRC1's abnormal expression in SCM using our data and external datasets. Furthermore, a neutrophil-related ceRNA network (AC145207.5/ miR-23a-3p/MRC1) was constructed and validated. Our findings reveal MRC1 as a potential NRHG in SCM pathogenesis, offering insights into neutrophil-mediated mechanisms in SCM and providing a novel molecular target for early diagnosis and intervention in SCM.

The multifaceted perspectives on the regulation of lncRNAs in hepatocellular carcinoma ferroptosis: from bench-to-bedside

Despite being characterized by high malignancy, high morbidity, and low survival rates, the underlying mechanism of hepatocellular carcinoma (HCC) has not been fully elucidated. Ferroptosis, a non-apoptotic form of regulated cell death, possesses distinct morphological, biochemical, and genetic characteristics compared to other types of cell death. Dysregulated actions within the molecular network that regulates ferroptosis have been identified as significant contributors to the progression of HCC. Long non-coding RNAs (lncRNAs) have emerged as influential contributors to diverse cellular processes, regulating gene function and expression through multiple mechanistic pathways. An increasing body of evidence indicates that deregulated lncRNAs are implicated in regulating malignant events such as cell proliferation, growth, invasion, and metabolism by influencing ferroptosis in HCC. Therefore, elucidating the inherent role of ferroptosis and the modulatory functions of lncRNAs on ferroptosis in HCC might promote the development of novel therapeutic interventions for this disease. This review provides a succinct overview of the roles of ferroptosis and ferroptosis-related lncRNAs in HCC progression and treatment, aiming to drive the development of promising therapeutic targets and biomarkers for HCC patients.

Long non-coding RNAs in ferroptosis and cuproptosis impact on prognosis and treatment in hepatocellular carcinoma

Ferroptosis and cuproptosis are recently discovered forms of cell death that have gained interest as potential cancer treatments, particularly for hepatocellular carcinoma. Long non-coding RNAs (lncRNAs) influence cancer cell activity by interacting with various nucleic acids and proteins. However, the role of ferroptosis and cuproptosis-related lncRNAs (FCRLs) in cancer remains underexplored. Ferroptosis and cuproptosis scores for each sample were assessed using Gene Set Variation Analysis (GSVA). Weighted correlation network analysis identified the FCRLs most relevant to our study. A risk model based on FCRLs was developed to categorize patients into high-risk and low-risk groups. We then compared overall survival (OS), tumor immune microenvironment, and clinical characteristics between these groups. The IPS score and ImmuCellAI webpage were used to predict the association between FCRL-related signatures and immunotherapy response. Finally, we validated the accuracy of FCRLs in hepatocellular carcinoma cell lines using induction agents (elesclomol and erastin). Patients in different risk subgroups showed significant differences in OS, immune cell infiltration, pathway activity, and clinical characteristics. Cellular assays revealed significant changes in the expression of AC019080.5, AC145207.5, MIR210HG, and LINC01063 in HCC cell lines following the addition of ferroptosis and cuproptosis inducers. We created a signature of four FCRLs that accurately predicted survival in HCC patients, laid the foundation for basic research related to ferroptosis and cuproptosis in hepatocellular carcinoma, and provided therapeutic recommendations for HCC patients.

MKLN1-AS promotes pancreatic cancer progression as a crucial downstream mediator of HIF-1α through miR-185-5p/TEAD1 pathway

In pancreatic ductal adenocarcinomas (PDAC), profound hypoxia plays key roles in regulating cancer cell behavior, including proliferation, migration, and resistance to therapies. The initial part of this research highlights the important role played by long noncoding RNA (lncRNA) MKLN1-AS, which is controlled by hypoxia-inducible factor-1 alpha (HIF-1α), in the progression of PDAC. Human samples of PDAC showed a notable increase in MKLN1-AS expression, which was linked to a worse outcome. Forced expression of MKLN1-AS greatly reduced the inhibitory impact on the growth and spread of PDAC cells caused by HIF-1α depletion. Experiments on mechanisms showed that HIF-1α influences the expression of MKLN1-AS by directly attaching to a hypoxia response element in the promoter region of MKLN1-AS.MKLN1-AS acts as a competitive endogenous RNA (ceRNA) by binding to miR-185-5p, resulting in the regulation of TEAD1 expression and promoting cell proliferation, migration, and tumor growth. TEAD1 subsequently enhances the development of PDAC. Our study results suggest that MKLN1-AS could serve as a promising target for treatment and a valuable indicator for predicting outcomes in PDAC. PDAC is associated with low oxygen levels, and the long non-coding RNA MKLN1-AS interacts with TEAD1 in this context.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma

Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.