A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

Role of Chondroitin Sulfate Proteoglycan 5 in Steroid-Induced Cataract

Steroid-induced cataracts (SIC) are defined as cataracts associated with the administration of corticosteroids. Long-term glucocorticoid treatment for inflammatory diseases reportedly increases the risk of SIC, and steroids can induce cataracts by disrupting ocular growth factor balance or homeostasis. In this study, we verified the effect of chondroitin sulfate proteoglycan 5 (CSPG5) using dexamethasone (dexa)-treated human lens epithelial (HLE-B3) cells and the lens epithelium from the anterior capsule of SIC patients obtained during cataract surgery. CSPG5 expression increased in the lens epithelium of SIC patients. The downregulation of CSPG5 suppressed the dexa-induced epithelial-mesenchymal transition (EMT)-related protein expression and motility in HLE-B3 cells. The disruption of the transcription factors EZH2 and B-Myb downregulated CSPG5, dexa-induced fibronectin expression, and cell migration in HLE-B3 cells, reaffirming that CSPG5 expression regulates EMT in lens epithelial cells. Taken together, these results indicate that the steroid-induced effects on lens epithelial cells are mediated via alterations in CSPG5 expression. Therefore, our study emphasizes the potential of CSPG5 as a therapeutic target for the prevention and treatment of SIC.

A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients.

Methods

We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR).

Results

We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend.

Conclusions

We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

PlexinA1 activation induced by β2-AR promotes epithelial-mesenchymal transition through JAK-STAT3 signaling in human gastric cancer cells

With the medical model shifting from a single biomedical model to a biopsychological-social model, the impact of psychosocial factors on cancer patients has attracted attention. Studies have shown that chronic stress caused by long-term psychological stress, such as anxiety and depression, can promote the malignant progression of tumors by acting on β2-adrenergic receptor (β2-AR). β2-AR can promote tumor migration by activating epithelial-mesenchymal transition (EMT). However, the underlying mechanisms in the regulation of EMT by β2-AR are still unclear. In this study, we established a chronic stress model by treating MGC-803 and SGC-7901 human gastric cancer cells with isoproterenol (ISO), a β2-AR agonist. EMT in the two gastric cancer cell lines was enhanced after ISO treatment. Thereafter, we found that the interaction between β2-AR and PlexinA1 was involved in the process by which chronic stress affects EMT in both MGC-803 and SGC-7901 cells. Moreover, the activation of β2-AR by ISO increased the expression of PlexinA1, activated JAK-STAT3 signaling and further promoted EMT in human gastric cancer cells. Importantly, the knockdown of PlexinA1 by small hairpin RNAs inhibited JAK-STAT3 signaling and abolished the EMT induced by β2-AR. In conclusion, PlexinA1 was an important downstream target of β2-AR, through which β2-AR promoted EMT in human gastric cancer cells by activating JAK-STAT3 signaling.

Stanniocalcin 2 (STC2): a universal tumour biomarker and a potential therapeutical target

Stanniocalcin 2 (STC2) is a glycoprotein which is expressed in a broad spectrum of tumour cells and tumour tissues derived from human breast, colorectum, stomach, esophagus, prostate, kidney, liver, bone, ovary, lung and so forth. The expression of STC2 is regulated at both transcriptional and post-transcriptional levels; particularly, STC2 is significantly stimulated under various stress conditions like ER stress, hypoxia and nutrient deprivation. Biologically, STC2 facilitates cells dealing with stress conditions and prevents apoptosis. Importantly, STC2 also promotes the development of acquired resistance to chemo- and radio- therapies. In addition, multiple groups have reported that STC2 overexpression promotes cell proliferation, migration and immune response. Therefore, the overexpression of STC2 is positively correlated with tumour growth, invasion, metastasis and patients' prognosis, highlighting its potential as a biomarker and a therapeutic target. This review focuses on discussing the regulation, biological functions and clinical importance of STC2 in human cancers. Future perspectives in this field will also be discussed.

The Bioinformatics-Based Analysis Identifies 7 Immune-Related Genes as Prognostic Biomarkers for Colon Cancer

Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.