Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.

A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

BACKGROUND

CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism.

METHODS

The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA). We construct a stemness-related metabolic gene signature through shrinkage estimation and univariate analysis, thereby calculating the corresponding risk scores. Moreover, we selected corresponding DEGs between groups with high- and low-risk score and conducted routine bioinformatic analyses. Furthermore, we validated the expression of four hub genes at the protein level through immunohistochemistry (IHC) in samples obtained from our patient cohort.

RESULTS

According to the findings, it was found that six genes-AKR1B10, GNA15, ALDH1B1, PLOD2, LPCAT1, and GPX8- were differentially expressed in both TCGA-CSEC and GEO datasets among 23 differentially expressed metabolism-related genes (DEMRGs). mRNAsi exhibited a notable association with the extent of key oncogene mutation. The results showed that the AUC values for forecasting survival at 1, 3, and 5 years are 0.715, 0.689, and 0.748, individually. We observed a notable association between the risk score and different immune cell populations, along with enrichment in crucial signaling pathways in CESC. Four genes differentially expressed between different risk score groups were validated by IHC to be highly expressed in the CESC samples at the protein level.

CONCLUSION

The current investigation indicated that a 3-gene signature based on stemness-related metabolic and 4 hub genes with differential expression between high and low-risk score subgroups may serve as valuable prognostic markers and potential therapeutic targets in CESC.

Noncanonical regulation of HOIL-1 on cancer stemness and sorafenib resistance identifies pixantrone as a novel therapeutic agent for HCC

BACKGROUND AND AIMS

Cancer stem cells (CSCs) contribute to therapy resistance in HCC. Linear ubiquitin chain assembly complex (LUBAC) has been reported to accelerate the progression of cancers, yet its role in the sorafenib response of HCC is poorly defined. Herein, we investigated the impact of LUBAC on sorafenib resistance and the CSC properties of HCC, and explored the potential targeted drugs.

APPROACH AND RESULTS

We found that HOIL-1, but not the other components of LUBAC, played a contributing role in LUBAC-mediated HCC sorafenib resistance, independent of its ubiquitin ligase activity. Both in vitro and in vivo assays revealed that the upregulated HOIL-1 expression enhanced the CSC properties of HCC. Mechanistically, HOIL-1 promoted sorafenib resistance and the CSC properties of HCC through Notch1 signaling. Mass spectrometry, co-immunoprecipitation, western blot, and immunofluorescence were used to determine that the A64/Q65 residues of HOIL-1 bound with the K78 residue of Numb, resulting in impaired Numb-mediated Notch1 lysosomal degradation. Notably, pixantrone was screened out by Autodock Vina, which was validated to disrupt HOIL-1/Numb interaction to inhibit Notch1 signaling and CSC properties by targeting the Q65 residue of HOIL-1. Moreover, pixantrone exerted synergistic effects with sorafenib for the treatment of HCC in different HCC mouse models.

CONCLUSIONS

HOIL-1 is critical in promoting sorafenib resistance and CSC properties of HCC through Notch1 signaling. Pixantrone targeting HOIL-1 restrains the sorafenib resistance and provides a potential therapeutic intervention for HCC.

Past, present, and future of chemically induced hepatocarcinogenesis rodent models: Perspectives concerning classic and new cancer hallmarks

Hepatocellular carcinoma (HCC), the main primary liver cancer, accounts for 5 % of all incident cases and 8.4 % of all cancer-related deaths worldwide. HCC displays a spectrum of environmental risk factors (viral chronic infections, aflatoxin exposure, alcoholic- and nonalcoholic fatty liver diseases) that result in molecular complexity and heterogeneity, contributing to a rising epidemiological burden, poor prognosis, and non-satisfactory treatment options. The emergence of HCC (i.e., hepatocarcinogenesis) is a multistep and complex process that addresses many (epi)genetic alterations and phenotypic traits, the so-called cancer hallmarks. "Polymorphic microbiomes", "epigenetic reprogramming", "senescent cells" and "unlocking phenotypic plasticity" are trending hallmarks/enabling features in cancer biology. As the main molecular drivers of HCC are still undruggable, chemically induced in vivo models of hepatocarcinogenesis are useful tools in preclinical research. Thus, this narrative review aimed at recapitulating the basic features of chemically induced rodent models of hepatocarcinogenesis, eliciting their permanent translational value regarding the "classic" and the "new" cancer hallmarks/enabling features. We gathered state-of-art preclinical evidence on non-cirrhotic, inflammation-, alcoholic liver disease- and nonalcoholic fatty liver-associated HCC models, demonstrating that these bioassays indeed express the recently added hallmarks, as well as reflect the interplay between classical and new cancer traits. Our review demonstrated that these protocols remain valuable for translational preclinical application, as they recapitulate trending features of cancer science. Further "omics-based" approaches are warranted while multimodel investigations are encouraged in order to avoid "model-biased" responses.

Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) and metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed to reveal the connection between metabolism reprogramming and the stemness characteristics of HCC, established a new gene signature related to stemness and metabolism and utilized it to assess HCC prognosis and immunotherapy response. The clinical information and gene expression profiles (GEPs) of 478 HCC patients came from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The one-class logistic regression (OCLR) algorithm was employed to calculate the messenger ribonucleic acid expression-based stemness index (mRNAsi), a new stemness index quantifying stemness features. Differentially expressed analyses were done between high- and low-mRNAsi groups and 74 differentially expressed metabolism-related genes (DEMRGs) were identified with the help of metabolism-related gene sets from Molecular Signatures Database (MSigDB). After integrated analysis, a risk score model based on the three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) and UDP-glucuronosyltransferase 1A5 (UGT1A5) was constructed and HCC patients were divided into high-risk and low-risk groups. Significant differences were found in pathway enrichment, immune cell infiltration patterns, and gene alterations between the two groups. High-risk group patients tended to have worse clinical outcomes and were more likely to respond to immunotherapy. A stemness-metabolism-related model composed of gender, age, the risk score model and tumor-node-metastasis (TNM) staging was generated and showed great discrimination and strong ability in predicting HCC prognosis and immunotherapy response.

Construction of an M2 macrophage-related prognostic model in hepatocellular carcinoma

Background

M2 macrophages play a crucial role in promoting tumor angiogenesis and proliferation, as well as contributing to chemotherapy resistance and metastasis. However, their specific role in the tumor progression of hepatocellular carcinoma (HCC) and their impact on the clinical prognosis remain to be further elucidated.

Materials and methods

M2 macrophage-related genes were screened using CIBERSORT and weighted gene co-expression network analysis (WGCNA), while subtype identification was performed using unsupervised clustering. Prognostic models were constructed using univariate analysis/least absolute shrinkage selector operator (LASSO) Cox regression. In addition, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and mutation analysis were used for further analysis. The relationship between the risk score and tumor mutation burden (TMB), microsatellite instability (MSI), the efficacy of transcatheter arterial chemoembolization (TACE), immunotype, and the molecular subtypes were also investigated. Moreover, the potential role of the risk score was explored using the ESTIMATE and TIDE (tumor immune dysfunction and exclusion) algorithms and stemness indices, such as the mRNA expression-based stemness index (mRNAsi) and the DNA methylation-based index (mDNAsi). In addition, the R package "pRRophetic" was used to examine the correlation between the risk score and the chemotherapeutic response. Finally, the role of TMCC1 in HepG2 cells was investigated using various techniques, including Western blotting, RT-PCR and Transwell and wound healing assays.

Results

This study identified 158 M2 macrophage-related genes enriched in small molecule catabolic processes and fatty acid metabolic processes in HCC. Two M2 macrophage-related subtypes were found and a four-gene prognostic model was developed, revealing a positive correlation between the risk score and advanced stage/grade. The high-risk group exhibited higher proliferation and invasion capacity, MSI, and degree of stemness. The risk score was identified as a promising prognostic marker for TACE response, and the high-risk subgroup showed higher sensitivity to chemotherapeutic drugs (e.g., sorafenib, doxorubicin, cisplatin, and mitomycin) and immune checkpoint inhibitor (ICI) treatments. The expression levels of four genes related to the macrophage-related risk score were investigated, with SLC2A2 and ECM2 showing low expression and SLC16A11 and TMCC1 exhibiting high expression in HCC. In vitro experiments showed that TMCC1 may enhance the migration ability of HepG2 cells by activating the Wnt signaling pathway.

Conclusion

We identified 158 HCC-related M2 macrophage genes and constructed an M2 macrophage-related prognostic model. This study advances the understanding of the role of M2 macrophages in HCC and proposes new prognostic markers and therapeutic targets.

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients

Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients.

Cell Pair Algorithm-Based Immune Infiltrating Cell Signature for Improving Outcomes and Treatment Responses in Patients with Hepatocellular Carcinoma

BACKGROUND

Immune interactions play important roles in the regulation of T cells' cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. A comprehensive analysis of immune cell types in HCC and immune-cell-related signatures predicting prognosis and monitoring immunotherapy efficacy is still absent.

METHODS

More than 1,300 hepatocellular carcinomas (HCC) patients were collected from public databases and included in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 28 immunocyte subpopulations. A cell pair algorithm was applied to construct an immune-cell-related prognostic index (ICRPI). Survival analyses were performed to measure the survival difference across ICRPI risk groups. Spearman's correlation analyses were used for the relevance assessment. A Wilcoxon test was used to measure the expression level's differences.

RESULTS

In this study, 28 immune subpopulations were retrieved, and 374 immune cell pairs (ICPs) were established, 38 of which were picked out by the least absolute shrinkage and selection operator (LASSO) algorithm. By using the selected ICPs, the ICRPI was constructed and validated to play crucial roles in survival stratification and dynamic monitoring of immunotherapy effect. We also explored several candidate drugs targeting ICRPI. A composite ICRPI and clinical prognostic index (ICPI) was then constructed, which achieved a more accurate estimation of HCC's survival and is a better choice for prognosis predictions in HCC.

CONCLUSIONS

In conclusion, we constructed and validated ICRPI based on the cell pair algorithm in this study, which might provide some novel insights for increasing the survival estimation and clinical response to immune therapy for individual HCC patients and contribute to the personalized precision immunotherapy strategy of HCC.

Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, which is associated with a variety of risk factors. Cancer stem cells are self-renewal cells, which can promote the occurrence and metastasis of tumors and enhance the drug resistance of tumor treatment. This study aimed to develop a stemness score model to assess the prognosis of hepatocellular carcinoma (HCC) patients for the optimization of treatment. The single-cell sequencing data GSE149614 was downloaded from the GEO database. Then, we compared the gene expression of hepatic stem cells and other hepatocytes in tumor samples to screen differentially expressed genes related to stemness. R package "clusterProfiler" was used to explore the potential function of stemness-related genes. We then constructed a prognostic model using LASSO regression analysis based on the TCGA and GSE14520 cohorts. The associations of stemness score with clinical features, drug sensitivity, gene mutation, and tumor immune microenvironment were further explored. R package "rms" was used to construct the nomogram model. A total of 18 stemness-related genes were enrolled to construct the prognosis model. Kaplan-Meier analysis proved the good performance of the stemness score model at predicting overall survival (OS) of HCC patients. The stemness score was closely associated with clinical features, drug sensitivity, and tumor immune microenvironment of HCC. The infiltration level of CD8+ T cells was lower, and tumor-associated macrophages were higher in patients with high-stemness score, indicating an immunosuppressive microenvironment. Our study established an 18 stemness-related gene model that reliably predicts OS in HCC. The findings may help clarify the biological characteristics and progression of HCC and help the future diagnosis and therapy of HCC.