The protective role of DMBT1 in cervical squamous cell carcinoma

Effects of conceptus proteins on endometrium and blood leukocytes of dairy cattle using transcriptome and meta-analysis

This study investigates the short and long-term effects of IFNT and PAG on the transcriptome of endometrium and blood leukocytes. Holstein heifers received intrauterine infusions of one of the following treatments: 20 mL of a 200 μg/mL bovine serum albumin solution (BSA; vehicle) from day 14 to 16 of the estrous cycle (BSA), vehicle + 10 μg/mL of IFNT from day 14 to 16 (IFNT3), vehicle + 10 μg/mL of IFNT from day 14 to 19 (IFNT6), and vehicle + 10 μg/mL of IFNT from day 14 to 16 followed by vehicle + 10 μg/mL of IFNT + 5 μg/mL of PAG from day 17 to 19 (IFNT+PAG). RNA-seq analysis was performed in endometrial biopsies and blood leukocytes collected after treatments. Acute IFNT signaling in the endometrium (IFNT3 vs BSA), induced differentially expressed genes (DEG) associated with interferon activation, immune response, inflammation, cell death, and inhibited vesicle transport and extracellular matrix remodeling. Prolonged IFNT signaling (IFNT6 vs IFNT3) altered gene expression related to cell invasion, retinoic acid signaling, and embryo implantation. In contrast, PAG induced numerous DEG in blood leukocytes but only 4 DEG in the endometrium. In blood leukocytes, PAG stimulated genes involved in development and TGFB signaling while inhibiting interferon signaling and cell migration. Overall, IFNT is a primary regulator of endometrial gene expression, while PAG predominantly affected the transcriptome of circulating immune cells during early pregnancy. Further research is essential to fully grasp the roles of identified DEG in both the endometrium and blood leukocytes.

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Background

The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).

Methods

The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.

Results

We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.

Conclusion

These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Background

Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.

Methods

Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method.

Results

The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control.

Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R² = 0.20, p < 0.001).

Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741.

As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively.

Conclusion

Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

DMBT1 suppresses cell proliferation, migration and invasion in ovarian cancer and enhances sensitivity to cisplatin through galectin-3/PI3k/Akt pathway

Ovarian cancer (OC) is one of the most common gynaecologic malignancies. Deleted in malignant brain tumors 1 (DMBT1) was considered as a tumour suppressor in multiple cancers, but there have been no systemic profiling studies of DMBT1 in OC until now. The aim of this study is to explore the role and the potential mechanism of DMBT1 in OC. mRNA levels and protein expressions of corresponding genes were detected by quantitative real-time polymerase chain reaction and western blot. Cell proliferation was detected by CCK-8 assay and cell colony formation. Cell migration and invasion were detected by wound healing and transwell assay. The combination between DMBT1 and galectin-3 was demonstrated by immunoprecipitation. We demonstrated that DMBT1 was downregulated in OC cell lines, especially SKOV3 cells. Overexpression of DMBT1 significantly inhibited cell proliferation, colony formation, migration and invasion, as well as decreased Matrix Metalloproteinase-2 (MMP-2) and MMP-7. DMBT1 caused a reduction of cell viability by treatment with cisplatin. Immunoprecipitation assay revealed a combination between DMBT1 and galectin-3. DMBT1 could decrease the expression of galectin-3 and inhibit the phosphorylation of PI3K and AKT, while overexpression of galectin-3 reversed this effect. In summary, DMBT1 might inhibit the progression of OC and improve the sensitivity of SKOV3 cells to cisplatin through galectin-3/PI3K/AKT pathway, giving a new insight into the role of DMBT1 in OC and enriching the potential strategies for OC treatment. SIGNIFICANCE OF THE STUDY: The present study focus on the role and the potential mechanism of DMBT1 in ovarian cancer (OC). We demonstrated that DMBT1 might inhibit the progression of ovarian by inhibiting cell proliferation, migration and invasion and increased the sensitivity to cisplatin through galectin-3/PI3K/AKT pathway. The findings ensure the interaction relation between DMBT1 and galectin-3 in OC, providing a novel biological marker for OC and enriching the potential strategies for OC treatment.