ZWINT is a Promising Therapeutic Biomarker Associated with the Immune Microenvironment of Hepatocellular Carcinoma

lncRNAs and cyclin-dependent kinases: Unveiling their critical roles in cancer progression

Long non-coding RNAs (lncRNAs) are a diverse class of RNA molecules that do not code for proteins but play critical roles in gene regulation. One such role involves the modulation of cell cycle progression and proliferation through interactions with cyclin-dependent kinases (CDKs), key regulators of cell division. Dysregulation of CDK activity is a hallmark of cancer, contributing to uncontrolled cell growth and tumor formation. These lncRNA-CDK interactions are part of a complex network of molecular mechanisms underlying cancer pathogenesis, involving various signaling pathways and regulatory circuits. Understanding the interplay between lncRNAs, CDKs, and cancer biology holds promise for developing novel therapeutic strategies targeting these molecular targets for more effective cancer treatment. Furthermore, targeting CDKs, key cell cycle progression and proliferation regulators, offers another avenue for disrupting cancer pathways and overcoming drug resistance. This can open new possibilities for individualized treatment plans and focused therapeutic interventions.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Background

Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.

Methods

From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.

Results

Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.

Conclusion

To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.