Cadherin Related Family Member 2 Acts As A Tumor Suppressor By Inactivating AKT In Human Hepatocellular Carcinoma

[Overexpression of CDHR2 inhibits proliferation of breast cancer cells by inhibiting the PI3K/Akt pathway]

OBJECTIVE

To investigate the mechanism by which CDHR2 overexpression inhibits breast cancer cell growth and cell cycle pragression via the PI3K/Akt signaling pathway.

METHODS

Bioinformatic analysis was performed to investigate CDHR2 expression in breast cancer and its correlation with survival outcomes of the patients. Immunohistochemistry was used to examine CDHR2 expressions in surgical specimens of tumor and adjacent tissues from 10 patients with breast cancer. CDHR2 expression levels were also detected in 5 breast cancer cell lines and a normal human mammary epithelial cell line using qRT-PCR and Western blotting. Breast cancer cell lines MDA-MB-231 and MCF7 with low CDHR2 expression were transfected with a CDHR2-overexpressing plasmid, and the changes in cell proliferation and cell cycle were evaluated using CCK-8 assay, EdU assay, and cell cycle assay; the changes in expressions of PI3K/Akt signaling pathway and cell cycle pathway proteins were detected with Western blotting.

RESULTS

Bioinformatic analysis showed low CDHR2 expression level in both breast cancer and adjacent tissues without significant difference between them (P > 0.05), but breast cancer patients with a high expression of CDHR2 had a more favorable prognosis. Immunohistochemistry, qRT-PCR and Western blotting showed that the expression of CDHR2 was significantly down-regulated in breast cancer tissues and breast cancer cells (P < 0.01), and its overexpression strongly inhibited cell proliferation, caused cell cycle arrest, and significantly inhibited PI3K and Akt phosphorylation and the expression of cyclin D1.

CONCLUSION

Overexpression of CDHR2 inhibits proliferation and causes cell cycle arrest in breast cancer cells possibly by inhibiting the PI3K/Akt signaling pathway.

Quantitative label-free proteomic analysis of excretory-secretory proteins in different developmental stages of Trichinella spiralis

Trichinella spiralis (T. spiralis) is a zoonotic parasitic nematode with a unique life cycle, as all developmental stages are contained within a single host. Excretory-secretory (ES) proteins are the main targets of the interactions between T. spiralis and the host at different stages of development and are essential for parasite survival. However, the ES protein profiles of T. spiralis at different developmental stages have not been characterized. The proteomes of ES proteins from different developmental stages, namely, muscle larvae (ML), intestinal infective larvae (IIL), preadult (PA) 6 h, PA 30 h, adult (Ad) 3 days post-infection (dpi) and Ad 6 dpi, were characterized via label-free mass spectrometry analysis in combination with bioinformatics. A total of 1217 proteins were identified from 9341 unique peptides in all developmental stages, 590 of which were quantified and differentially expressed. GO classification and KEGG pathway analysis revealed that these proteins were important for the growth of the larvae and involved in energy metabolism. Moreover, the heat shock cognate 71 kDa protein was the centre of protein interactions at different developmental stages. The results of this study provide comprehensive proteomic data on ES proteins and reveal that these ES proteins were differentially expressed at different developmental stages. Differential proteins are associated with parasite survival and the host immune response and may be potential early diagnostic antigen or antiparasitic vaccine candidates.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Phosphatidylinositol glycan anchor biosynthesis, class C is a prognostic biomarker and correlates with immune infiltrates in hepatocellular carcinoma

Background and aims: The exact function of Phosphatidylinositol Glycan Anchor Biosynthesis, Class C (PIGC) gene has yet to be elucidated. In the study, we attempted to clarify the correlations of PIGC to prognosis and tumor-infiltrating lymphocytes in hepatocellular carcinoma (HCC).

Methods:PIGC expression was analyzed via the Oncomine database, Gene Expression Profiling Interactive Analysis, Hepatocellular carcinoma data base, Human Protein Atlas database and Tumor Immune Estimation Resource (TIMER). We showed the correlation of PIGC with the clinical characteristics using UALCAN. We evaluated the influence of PIGC on clinical prognosis using Kaplan-Meier plotter databases. And co-expressed genes with PIGC and its regulators were identified using LinkedOmics. The correlations between PIGC and cancer immune infiltrates were investigated via TIMER. We analyzed the drug sensitivity and immunotherapy response via R package.

Results:PIGC was found up-regulated in tumor tissues in multiple HCC cohorts, also increased in HCC patient with different clinical characteristics. High PIGC expression was associated with poorer overall survival. PIGC expression showed a strong positive association with the expression of ACBD6, a strong negative association with AGXT212. The cell components and distribution in treatment and non-treatment of HCC patients were quite distinct, which may reveal the relationship between the immunotherapy with tumor microenvironment. Notably, PIGC expression was positively correlated with infiltrating levels of immune cells.

Conclusion: These findings suggest that PIGC is correlated with prognosis and immune infiltrating in HCC, which can be used as a prognostic biomarker for determining prognosis, laying a foundation for further study of the immune regulatory role of PIGC in HCC.

Intermittent fasting induces chronic changes in the hepatic gene expression of Red Jungle Fowl (Gallus gallus)

Background

Intermittent fasting (IF), the implementation of fasting periods of at least 12 consecutive hours on a daily to weekly basis, has received a lot of attention in recent years for imparting the life-prolonging and health-promoting effects of caloric restriction with no or only moderate actual restriction of caloric intake. IF is also widely practiced in the rearing of broiler breeders, the parent stock of meat-type chickens, who require strict feed restriction regimens to prevent the serious health problems associated with their intense appetites. Although intermittent fasting has been extensively used in this context to reduce feed competition and its resulting stress, the potential of IF in chickens as an alternative and complementary model to rodents has received less investigation. In both mammals and birds, the liver is a key component of the metabolic response to IF, responding to variations in energy balance. Here we use a microarray analysis to examine the liver transcriptomics of wild-type Red Jungle Fowl chickens fed either ad libitum, chronically restricted to around 70% of ad libitum daily or intermittently fasted (IF) on a 2:1 (2 days fed, 1 day fasted) schedule without actual caloric restriction. As red junglefowl are ancestral to domestic chicken breeds, these data serve as a baseline to which existing and future transcriptomic results from farmed birds such as broiler breeders can be compared.

Results

We find large effects of feeding regimen on liver transcriptomics, with most of the affected genes relating to energy metabolism. A cluster analysis shows that IF is associated with large and reciprocal changes in genes related to lipid and carbohydrate metabolism, but also chronic changes in genes related to amino acid metabolism (generally down-regulated) and cell cycle progression (generally up-regulated). The overall transcription pattern appears to be one of promoting high proliferative plasticity in response to fluctuations in available energy substrates. A small number of inflammation-related genes also show chronically changed expression profiles, as does one circadian rhythm gene.

Conclusions

The increase in proliferative potential suggested by the gene expression changes reported here indicates that birds and mammals respond similarly to intermittent fasting practices. Our findings therefore suggest that the health benefits of periodic caloric restriction are ubiquitous and not restricted to mammals alone. Whether a common fundamental mechanism, for example involving leptin, underpins these benefits remains to be elucidated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08533-5.

Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques.

METHODS

We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers.

RESULTS

In this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified.

CONCLUSIONS

It is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What's more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

Proteomics analysis of tissue small extracellular vesicles reveals protein panels for the reoccurrence prediction of colorectal cancer

Many stage II/III colorectal cancer (CRC) patients might relapse after routine treatment and there is a great need of reliable biomarkers for predicting its reoccurrence risk. Small extracellular vesicles (sEVs) could regulate many pathophysiological processes of diseases, which are promising source for biomarker discovery. In this study, we implemented a MS-based workflow that utilizes data-dependent acquisition (DDA) for discovery and parallel reaction monitoring (PRM) for validation of high relapse risk related biomarkers. We compared the protein profiling of sEVs from CRC tissues and paired adjacent tissues in relapsed group (n = 5) and non-relapsed group (n = 5). 417 and 1140 proteins were differentially expressed between the tumor tissues and adjacent tissues in relapsed group and non-relapsed group, respectively. Bioinformatics analysis showed that immunity of the relapsed patients (Z-score - 0.69) was relatively poorer than the non-relapsed patients (Z-score 2.59), while chronic inflammatory response was activated (Z-score 3.0), which might enhance the reoccurrence risk. Four proteins (HLA-DPA1, S100P, NUP205, PCNA) showed significant expressions in the adjacent tissues of the relapsed group by PRM validation. ROC analysis of HLA-DPA1 (AUC = 0.96) achieved the best classification accuracy in separating the relapsed group and the non-relapsed group. Our data demonstrate that tissue-derived sEVs harbor prognostic proteomic signatures of CRC. SIGNIFICANCE: In this research, our proteomics analysis of tissue sEVs revealed that poor immunity as well as chronic inflammatory of the CRC relapsed patient likely lead to poor prognosis and high risk of reoccurrence. The significant expression levels of four proteins (HLA-DPA1, S100P, NUP205, PCNA) in the adjacent tissues of the relapsed group might be used to predict the risk of relapse in postoperative follow-ups.