Increased DEF6 expression is correlated with metastasis and poor prognosis in human osteosarcoma

DEF6 has potential to be a biomarker for cancer prognosis: A pan-cancer analysis

Introduction

DEF6 is a gene associated with the immune system and is thought to play a crucial role in autoimmunity. There are few DEF6-related studies in cancer, and it is assumed that DEF6 is a proto-oncogene. There is currently no pan-cancer analysis of DEF6, and we performed a systematic and comprehensive pan-cancer analysis of DEF6 in an attempt to reveal its role and function in cancer.

Methods

The data were analyzed by mining databases available to the public and by using R software. Moreover, immunohistochemistry was used to validate the results.

Results

Our results revealed that DEF6 is commonly aberrantly expressed in cancer and its expression is strongly correlated with survival prognosis in a variety of cancer types. Through correlation analysis we found that DEF6 was associated with multiple immune genes and was closely related to immune infiltration. In the enrichment analysis, DEF6 may have cross-talk with multiple cancer pathways and exert oncogenic or pro-cancer functions. In addition, we collected pathological samples from colorectal cancer patients for immunohistochemical analysis and found that patients with higher immunohistochemical scores had more lymph node metastases, higher CA199, and bigger tumor size.

Discussion

Overall, DEF6 expression is closely related to cancers and has the potential to act as a cancer biomarker.

A novel immune prognostic model of non-M3 acute myeloid leukemia

Acute myeloid leukemia (AML) is a common hematological malignancy in adults. AML patients exhibit clinical heterogeneity with complications of molecular basis. The leukemogenesis of AML involves immune escape, and the immunosuppression status of the patient might have great impact on AML treatment outcome. In this study, we established an immune prognostic model of AML using bioinformatics tools. With the data in the TCGA and GTEx datasets, we analyzed differentially expressed genes (DEGs) in non-M3 AML and identified 420 immune-related DEGs. Among which, 49 genes' expression was found to be related to AML prognosis based on univariate Cox regression analysis. Next, we established a prognostic model with these 49 genes in AML by LASSO regression and multivariate Cox regression analyses. In our model, the expressions of 5 immune genes, MIF, DEF6, OSM, MPO, AVPR1B, were used to stratify non-M3 AML patients' treatment outcome. A patient's risk score could be calculated as Risk Score=0.40081 × MIF (MIF expression) - 0.15201 × MPO + 0.78073 × DEF6 - 0.45192 × AVPR1B + 0.25912 × OSM. The area under the curve of the risk score signature was 0.8, 0.8, and 0.96 at 1 year, 3 years, and 5 years, respectively. The prognostic model was then validated internally by TCGA data and externally by GEO data. At last, the result of single-sample gene-set enrichment analysis demonstrated that compared with healthy samples, the abundance of non-turmeric immune cells was significantly repressed in AML. To summarize, we presented an immune-related 5-gene signature prognostic model in AML.

Proteomic Analysis Reveals Molecular Differences in the Development of Gastric Cancer

Gastric cancer (GC) is the 3rd leading cause of death from cancer and the 5th most common cancer worldwide. The detection rate of GC among Tibetans is significantly higher than that in Han Chinese, probably due to differences in their living habits, dietary structure, and environment. Despite such a high disease burden, the epidemiology of gastric cancer has not been studied in this population. Molecular markers are required to aid the diagnosis and treatment of GC. In this study, we collected gastric tissue samples from patients in Tibet with chronic nonatrophic gastritis (CNAG) (n = 6), chronic atrophic gastritis (CAG) (n = 7), gastric intraepithelial neoplasia (GIN) (n = 4), and GC (n = 5). The proteins in each group were analyzed using coupled label-free mass spectrometry. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein interaction networks were used to analyze the differentially expressed proteins (DEPs) among groups. DEPs were quantified in comparisons of GC versus CNAG (223), GC versus GIN (100), and GIN versus CNAG (341). GO and KEGG analyses showed that the DEPs were mainly associated with immunity (GC versus CNAG) and cancer proliferation and metastasis (GC versus GIN, and GIN versus CNAG). Furthermore, the expression levels of cell proliferation and cytoskeleton-related proteins increased consistently during cancer development, such as ITGA4, DDC, and CPT1A; thus, they are potential diagnostic markers. These results obtained by proteomics analysis could improve our understanding of cancer biology in GC and provide a rich resource for data mining and discovering potential immunotherapy targets.