Combining Recombinase-Mediated Cassette Exchange Strategy with Quantitative Proteomic and Phosphoproteomic Analyses to Inspect Intracellular Functions of the Tumor Suppressor Galectin-4 in Colorectal Cancer Cells

Targeting intracellular galectins for cancer treatment

Although considerable attention has been paid to the role of extracellular galectins in modulating, positively or negatively, tumor growth and metastasis, we have witnessed a growing interest in the role of intracellular galectins in response to their environment. This is not surprising as many galectins preferentially exist in cytosolic and nuclear compartments, which is consistent with the fact that they are exported outside the cells via a yet undefined non-classical mechanism. This review summarizes our most recent knowledge of their intracellular functions in cancer cells and provides some directions for future strategies to inhibit their role in cancer progression.

Seven bacterial response-related genes are biomarkers for colon cancer

BACKGROUND

Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.

METHODS

The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.

RESULTS

A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).

CONCLUSIONS

We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Proteomics and Its Applications in Cancers

Cancer is a system malignant transformation that covers a wide group of diseases and can affect any organ of the human body [...].

Integrative proteomics and phosphoproteomics profiling on osteogenic differentiation of periodontal ligament stem cell

This study aims to elucidate the phosphorylated profile of periodontal ligament stem cells (PDLSCs) osteogenic differentiation, which contributes to the promotion of periodontium regeneration. PDLSCs cultured in the osteogenic induction medium for 14 days were analyzed by proteomics and phosphoproteomics. Potential functions of phosphorylated differentially expressed proteins (DEPs) were annotated and enriched based on Gene Ontology (GO). Furtherly, overlapped DEPs were identified and conducted protein-protein interaction (PPI) network united with the top 20 up/downregulated phosphorylated DEPs. Hub phosphorylated DEPs were analyzed by Cytoscape, and the protein kinase phosphorylation network was predicted by iGPS. Proteomics identified 87 upregulated and 227 downregulated DEPs. Phosphoproteomics identified 460 upregulated and 393 downregulated phosphorylated DEPs, and they were primarily enriched in mitochondrial function and ion-channel related terms. Furthermore, 63 overlapped DEPs were recognized for more accurate predictions. Among the top 10 hub phosphorylated DEPs, only Integrin alpha-5 (ITGA5) expressed upregulated phosphorylation, and half of them belonged to extracellular matrix (ECM) proteins. In addition, numerous kinases corresponding to four interactive hub phosphorylated DEPs were predicted, including Collagen alpha-2(I) (COL1A2), Syndecan-1 (SDC1), Fibrillin-1 (FBN1), and ITGA5. Our findings established a basis for further elucidation of the phosphorylation of PDLSCs osteogenic differentiation, and COL1A2/SDC1/ITGA5/FBN1 phosphorylated network may dominate this process.