Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis

Discovery of candidate biomarkers from plasma-derived extracellular vesicles of patients with cirrhosis and hepatocellular carcinoma: an exploratory proteomic study

Extracellular vesicles (EVs) represent an attractive source of biomarkers due to their biomolecular cargo. The aim of this study was to identify candidate protein biomarkers from plasma-derived EVs of patients with liver cirrhosis (LC) and hepatocellular carcinoma (HCC). Plasma-derived EVs from healthy participants (HP), LC, and HCC patients (eight samples each) were subjected to label-free quantitative proteomic analysis using LC-MS/MS. A total of 248 proteins were identified, and differentially expressed proteins (DEPs) were obtained after pairwise comparison. We found that DEPs mainly involve complement cascade activation, coagulation pathways, cholesterol metabolism, and extracellular matrix components. By choosing a panel of up- and down-regulated proteins involved in cirrhotic and carcinogenesis processes, TGFBI, LGALS3BP, C7, SERPIND1, and APOC3 were found to be relevant for LC patients, while LRG1, TUBA1C, TUBB2B, ACTG1, C9, HP, FGA, FGG, FN1, PLG, APOB and ITIH2 were associated with HCC patients, which could discriminate both diseases. In addition, we identified the top shared proteins in both diseases, which included LCAT, SERPINF2, A2M, CRP, and VWF. Thus, our exploratory proteomic study revealed that these proteins might play an important role in the disease progression and represent a panel of candidate biomarkers for the prognosis and diagnosis of LC and HCC.

Hyaluronic Acid Interacting Molecules Mediated Crosstalk between Cancer Cells and Microenvironment from Primary Tumour to Distant Metastasis

Hyaluronic acid (HA) is a prominent component of the extracellular matrix, and its interactions with HA-interacting molecules (HAIMs) play a critical role in cancer development and disease progression. This review explores the multifaceted role of HAIMs in the context of cancer, focusing on their influence on disease progression by dissecting relevant cellular and molecular mechanisms in tumour cells and the tumour microenvironment. Cancer progression can be profoundly affected by the interactions between HA and HAIMs. They modulate critical processes such as cell adhesion, migration, invasion, and proliferation. The TME serves as a dynamic platform in which HAIMs contribute to the formation of a unique niche. The resulting changes in HA composition profoundly influence the biophysical properties of the TME. These modifications in the TME, in conjunction with HAIMs, impact angiogenesis, immune cell recruitment, and immune evasion. Therefore, understanding the intricate interplay between HAIMs and HA within the cancer context is essential for developing novel therapeutic strategies. Targeting these interactions offers promising avenues for cancer treatment, as they hold the potential to disrupt critical aspects of disease progression and the TME. Further research in this field is imperative for advancing our knowledge and the treatment of cancer.

Development and Validation of a Propionate Metabolism-Related Gene Signature for Prognostic Prediction of Hepatocellular Carcinoma

Background

Studies have demonstrated that propionate metabolism-related genes (PMRGs) are associated with cancer progression. PMRGs are not known to be involved in Hepatocellular carcinoma (HCC).

Methods

In this study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were accessed for HCC-related transcriptome data and clinical information. First, DE-PMRGs were derived by intersecting PMRGs and DEGs between HCC tissues and normal controls. The clusterProfiler R package was then used to enrich DE-PMRGs. In addition, biomarkers of HCC were identified, and a prognostic model was developed. Using functional analysis and tumor microenvironment analysis, new insights were obtained into HCC. The expression of biomarkers was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

132 DE-PMRGs were obtained by intersecting 3690 DEGs and 291 PMRGs. Steroid and organic acid metabolism were associated with these genes. For the construction of the risk model for HCC samples, five biomarkers were identified, including Acyl-CoA dehydrogenase short chain (ACADS), CYP19A1, formiminotransferase cyclodeaminase (FTCD), glucose-6-phosphate dehydrogenase (G6PD), and glutamic-oxaloacetic transaminase (GOT2). ACADS, FTCD, and GOT2 were positive factors, whereas CYP19A1 and G6PD were negative. HCC patients with AUC greater than 0.6 were predicted to survive 1/2/3/4/5 years, indicating decent efficiency of the model. The probability of 1/3/5-survival for HCC was also predicted by the nomogram using the risk score, pathologic T stage, and cancer status. Moreover, functional enrichment analysis revealed the high-risk genes were associated with invasion and epithelial-mesenchymal transition. Significantly, immune cell infiltration and immune checkpoint expression were linked to HCC development.

Conclusion

This study identified five biomarkers of propionate metabolism that can predict HCC prognosis. This finding may provide a deeper understanding of PMRG function in HCC.

Decoding the significant diagnostic and prognostic importance of maternal embryonic leucine zipper kinase in human cancers through deep integrative analyses

BACKGROUND

Cancer is a multifactorial disease and the second leading cause of human deaths worldwide. So far, the underlying mechanisms of cancer have not been yet fully elucidated.

METHODS

By using TCGA expression data, we determine the pathogenic roles of the maternal embryonic leucine zipper kinase (MELK) gene in various human cancers in this study. For this purpose, different online databases and tools (UALCAN, Kaplan-Meier (KM) plotter, TNMplot, GENT2, GEPIA, HPA, cBioPortal, STRING, Enrichr, TIMER, Cytoscape, DAVID, MuTarget, and CTD) were used.

RESULTS

MELK gene expression was analyzed in a total of 24 human cancers and was found notably up-regulated in all the 24 analyzed tumor tissues relative to controls. Moreover, across a few specific cancers, including kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), lung adenocarcinoma (LUAD), and liver hepatocellular carcinoma (LIHC) patients, MELK up-regulation was observed to be correlated with the shorter survival duration and metastasis. This valuable information highlighted that MELK plays a significant role in the development and progression of these four cancers. Based on clinical variables, MELK higher expression was also found in KIRC, STAD, LUAD, and LIHC patients with different clinical variables. Gene ontology and pathway analysis outcomes showed that MELK-associated genes notably co-expressed with MELK and belongs to a variety of diverse biological processes, molecular functions, and pathways. MELK expression was also correlated with promoter methylation levels, genetic alterations, other mutant genes, tumor purity, CD8+ T, and CD+4 T immune cells infiltrations in KIRC, STAD, LUAD, and LIHC.

CONCLUSION

This pan-cancer study revealed the diagnostic and prognostic roles of MELK across four different cancers.

Alpha-2-Heremans-Schmid-glycoprotein (AHSG) a potential biomarker associated with prognosis of chromophobe renal cell carcinoma: The PROPOLIS study

Background and Aims

Chromophobe renal cell carcinoma (chRCC) is the third common pathological subtype in renal cancers. However, the underlying mechanisms of specific genetic characteristics of chRCC are currently unclear. In this study, protein expression profiles, gene ontology (GO), and survival plots were provided by integrated bioinformatics analysis to investigate key genes associated with the mechanism of tumorigenesis and prognosis of chRCC.

Methods

The chRCC data set of gene expression profiles and clinical data were obtained from the gdc-client (https://portal.gdc.cancer.gov) deposited on The Cancer Genome Atlas (TCGA) data portal. Differentially expressed genes (DEGs) in chRCC, compared with normal samples, were analyzed by R packages "DESeq2," "edgeR," and "limma." Heat maps, volcano plots, and principal component analysis (PCA) were performed for integrated analyses. GUniGO, mutant analysis, and survival plots were performed by R packages. A protein-protein interaction (PPI) network was generated and analyzed by R packages, online String software, and Cytoscape software. Survival analysis and gene expressing comparison in tumor and normal samples were used to detect the core genes of chRCC. Furthermore, the top interacting proteins were reanalyzed.

Results

A total of 306 upregulated genes and 678 downregulated genes were identified by a Venn diagram. Ten hub genes were extracted from PPI network. Furthermore, Alpha-2-Heremans-Schmid-glycoprotein (AHSG), one of 10 hub genes, was found to be associated with chRCC, and had a big difference in expression between survival and dead events. AHSG could predict potential prognostic and may be a diagnostic biomarker in chRCC.

Conclusion

This study illustrated that AHSG may be a potential therapeutic target and prognostic genetic marker for chRCC.

Classification and Prognostic Characteristics of Hepatocellular Carcinoma Based on Glycolysis Cholesterol Synthesis Axis

Background

Liver hepatocellular carcinoma (LIHC) is among the most frequent causes of cancer-related death across the world with a considerably poor prognosis. The current study targeted providing a new type of LIHC from the perspective of the glycolysis/cholesterol synthesis axis, predicting its prognostic characteristics, and exploring the potential role and mechanism of the glycolysis/cholesterol synthesis axis in the occurrence and development of LIHC.

Methods

Based on the two expression profile data and clinical information of LIHC in The Cancer Genome Atlas (TCGA) database and hepatocellular carcinoma database (HCCDB), as well as glycolysis/cholesterol-related genes from the Molecular Signatures Database (MSigDB), unsupervised consistent clustering method was used to identify molecular subtypes. In addition, the differential genes were identified by limma package, and then the gene set was enriched, analyzed, and annotated by WebGestaltR package. At the same time, the immune infiltration analysis of tumor samples was carried out using the ESTIMATE to evaluate the tumor immune score of the samples. Finally, the differences in clinical characteristics among molecular subtypes were measured using univariate and multivariate Cox analyses.

Results

According to the median standardized expression levels of glycolysis/cholesterol production genes, samples were divided into four groups (molecular subtypes): Quiescent group, Glycolysis group, Cholesterol group, and Mixed group. Significant prognostic differences were observed among the four groups. In both TCGA and HCCDB18 datasets, the prognosis of subtype Mixed was the worst, while Quiescent had a good prognosis. Cell cycle and oncogenic pathways were significantly enriched in the Mixed group. In addition, glycolysis and cholesterol production gene expressions were related to the prognostic LIHC subtype classification genes' expression levels.

Conclusion

Metabolic classification regarding glycolysis and cholesterol production pathways provided new insights into the biological aspects of LIHC molecular subtypes and might help to develop personalized therapies for unique tumor metabolic profiles.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

Lysine demethylase 5C epigenetically reduces transcription of ITIH1 that results in augmented progression of liver hepatocellular carcinoma

Lysine demethylase 5C (KDM5C) is a member of the KDM family of demethylases and has been reported as a cancer driver. This study aimed to probe the function of KDM5C in the development of liver hepatocellular carcinoma (LIHC) and the molecules of action. According to data from publicly accessible bioinformatic databases, KDM5C is highly expressed in LIHC and associated with poor patient prognosis. High expression of KDM5C was detected in acquired LIHC cell lines. Downregulation of KDM5C weakened proliferation, migration, invasiveness, and resistance to death of the LIHC cells in vitro, and it reduced growth of the xenograft tumors in nude mice. Inter-alpha-trypsin inhibitor heavy chain 1 (ITIH1) was predicted as a downstream gene negatively regulated by KDM5C. KDM5C-regulated H3K4me1 modification at the promoter region of ITIH1, inducing its transcriptional inactivation. Further downregulation of ITIH1 in cancer cells blocked the functions of KDM5C silencing and restored the malignant behaviors of LIHC cells. The activity of the PI3K/AKT signaling was decreased following KDM5C downregulation but recovered upon ITIH1 silencing. In conclusion, this study suggested that KDM5C epigenetically reduces ITIH1 and activates the PI3K/AKT signaling pathway to promote LIHC progression.