Identification of key genes associated with the progression of intrahepatic cholangiocarcinoma using weighted gene co-expression network analysis

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

Cell-Wall-Degrading Enzymes-Related Genes Originating from Rhizoctonia solani Increase Sugar Beet Root Damage in the Presence of Leuconostoc mesenteroides

Sugar beet crown and root rot caused by Rhizoctonia solani is a major yield constraint. Root rot is highly increased when R. solani and Leuconostoc mesenteroides co-infect roots. We hypothesized that the absence of plant cell-wall-degrading enzymes in L. mesenteroides and their supply by R. solani during close contact, causes increased damage. In planta root inoculation with or without cell-wall-degrading enzymes showed greater rot when L. mesenteroides was combined with cellulase (22 mm rot), polygalacturonase (47 mm), and pectin lyase (57 mm) versus these enzymes (0-26 mm), R. solani (20 mm), and L. mesenteroides (13 mm) individually. Carbohydrate analysis revealed increased simpler carbohydrates (namely glucose + galactose, and fructose) in the infected roots versus mock control, possibly due to the degradation of complex cell wall carbohydrates. Expression of R. solani cellulase, polygalacturonase, and pectin lyase genes during root infection corroborated well with the enzyme data. Global mRNAseq analysis identified candidate genes and highly co-expressed gene modules in all three organisms that might be critical in host plant defense and pathogenesis. Targeting R. solani cell-wall-degrading enzymes in the future could be an effective strategy to mitigate root damage during its interaction with L. mesenteroides.

A2M is a potential core gene in intrahepatic cholangiocarcinoma

Background

Intrahepatic cholangiocarcinoma (ICC) is a type of malignant tumor ranking the second in the incidence of primary liver cancer following hepatocellular carcinoma. Both the morbidity and mortality have been increasing in recent years. Small duct type of ICC has potential therapeutic targets. But overall, the prognosis of patients with ICC is usually very poor.

Methods

To search latent therapeutic targets for ICC, we programmatically selected the five most suitable microarray datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE45001 and GSE76311) collected from the Gene Expression Omnibus (GEO) database. The GEO2R tool was effective to find out differentially expressed genes (DEGs) between ICC and normal tissue. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were executed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze protein–protein interaction of these DEGs and protein–protein interaction of these DEGs was modified by Cytoscape3.8.2. Survival analysis was performed using Gene Expression Profiling Interactive Analysis (GEPIA) online analysis tool.

Results

A total of 28 upregulated DEGs and 118 downregulated DEGs were screened out. Then twenty hub genes were selected according to the connectivity degree. The survival analysis results showed that A2M was closely related to the pathogenesis and prognosis of ICC and was a potential therapeutic target for ICC.

Conclusions

According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients. The value of A2M in the treatment of ICC needs to be further studied.

Molecular Signatures Correlated With Poor IVF Outcomes: Insights From the mRNA and lncRNA Expression of Endometriotic Granulosa Cells

The outcomes of in vitro fertilization (IVF) for endometriotic women are significantly worse than for patients without ovarian endometriosis (OEM), as shown by fewer retrieved oocytes. However, the exact pathophysiological mechanism is still unknown. Thus, we conducted a prospective study that analyzed mRNA and lncRNA transcriptome between granulosa cells (GCs) from patients with fewer retrieved oocytes due to OEM and GCs from controls with male factor (MF) infertility using an RNA sequencing approach. We found a group of significantly differentially expressed genes (DEGs), including NR5A2, MAP3K5, PGRMC2, PRKAR2A, DEPTOR, ITGAV, KPNB1, GPC6, EIF3A, and SMC5, which were validated to be upregulated and negatively correlated with retrieved oocyte numbers in GCs of patients with OEM, while DUSP1 demonstrated the opposite. The molecular functions of these DEGs were mainly enriched in pathways involving mitogen-activated protein kinase (MAPK) signaling, Wnt signaling, steroid hormone response, apoptosis, and cell junction. Furthermore, we performed lncRNA analysis and identified a group of differentially expressed known/novel lncRNAs that were co-expressed with the validated DEGs and correlated with retrieved oocyte numbers. Co-expression networks were constructed between the DEGs and known/novel lncRNAs. These distinctive molecular signatures uncovered in this study are involved in the pathological regulation of ovarian reserve dysfunction in OEM patients.

Identification of metabolism genes related to hepatocarcinogenesis and progression in type 2 diabetes mellitus via co-expression networks analysis

Background

Type 2 Diabetes Mellitus (T2DM) is an independent risk factor of hepatocellular carcinoma (HCC). However, the related genes and modules to hepatocarcinogenesis and progression in T2DM remain unclear.

Methods

The microarray data from Gene Expression Omnibus (GEO) were analyzed to screen differentially expressed genes (DEGs) of T2DM and HCC dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed on these DEGs to detect the modules and genes, respectively. Common genes in modules with clinical interests of T2DM and HCC were obtained and annotated via GOSemSim package and Metascape. Genes related to late-stage HCC and high glycated haemoglobin (HbA1c) were also identified. These genes were validated by UALCAN analysis and univariate cox regression based on The Cancer Genome Atlas (TCGA). Finally, another two independent datasets were applied to confirm the results of our study.

Results

A total of 1288 and 1559 DEGs of T2DM and HCC were screened, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed several shared pathways in two diseases, such as pathways in cancer and metabolism. A total of 37 common genes correlated with T2DM and HCC were then identified with WGCNA. Furthermore, 12 genes from modules associated with late-stage HCC and high HbA1c were regarded as hub genes. Among these genes, 8 genes associated with tumor invasion and metastasis were validated by UALCAN analysis. Moreover, downregulations of ACAT1, SLC2A2, PCK1 and ABAT were significantly associated with poorer prognosis in HCC patients with elevated HbA1c. Additionally, the expressions of PCK1 and ABAT were raised in HepG2 cells pre-treated with metformin and phenformin.

Conclusions

The present study confirmed several metabolic genes related to hyperglycemia and malignant tumor, which may provide not only new insights into the pathogenesis of hepatocarcinogenesis and progression in T2DM, but also novel therapeutic targets for T2DM patients with HCC in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00177-x.

Bioinformatics analysis identified CDC20 as a potential drug target for cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) is a malignancy that originates from bile duct cells. The incidence and mortality of CCA are very high especially in Southeast Asian countries. Moreover, most CCA patients have a very poor outcome. Presently, there are still no effective treatment regimens for CCA. The resistance to several standard chemotherapy drugs occurs frequently; thus, searching for a novel effective treatment for CCA is urgently needed.

METHODS

In this study, comprehensive bioinformatics analyses for identification of novel target genes for CCA therapy based on three microarray gene expression profiles (GSE26566, GSE32225 and GSE76297) from the Gene Expression Omnibus (GEO) database were performed. Based on differentially expressed genes (DEGs), gene ontology and pathway enrichment analyses were performed. Protein-protein interactions (PPI) and hub gene identifications were analyzed using STRING and Cytoscape software. Then, the expression of candidate genes from bioinformatics analysis was measured in CCA cell lines using real time PCR. Finally, the anti-tumor activity of specific inhibitor against candidate genes were investigated in CCA cell lines cultured under 2-dimensional and 3-dimensional cell culture models.

RESULTS

The three microarray datasets exhibited an intersection consisting of 226 DEGs (124 up-regulated and 102 down-regulated genes) in CCA. DEGs were significantly enriched in cell cycle, hemostasis and metabolism pathways according to Reactome pathway analysis. In addition, 20 potential hub genes in CCA were identified using the protein-protein interaction (PPI) network and sub-PPI network analysis. Subsequently, CDC20 was identified as a potential novel targeted drug for CCA based on a drug prioritizing program. In addition, the anti-tumor activity of a potential CDC20 inhibitor, namely dinaciclib, was investigated in CCA cell lines. Dinaciclib demonstrated huge anti-tumor activity better than gemcitabine, the standard chemotherapeutic drug for CCA.

CONCLUSION

Using integrated bioinformatics analysis, CDC20 was identified as a novel candidate therapeutic target for CCA.

Identification of prognostic biomarkers associated with metastasis and immune infiltration in osteosarcoma

Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.