ABSTRACT
Background: Gastric cancer (GC) is one of the most common and poor prognosis malignancy in the world. The Family with sequence similarity 83 (FAM83) comprises of eight members of A-H. Accumulating evidence confirmed important roles for FAM83 family in tumorigenesis and progression. However, the prognostic values of FAM83 family in GC still have not been clarified. Methods: ONCOMINE, UALCAN, GEPIA, THE HUMAN PROTEIN ATLAS, Kaplan-Meier Plotter, cBioPortal, DAVID, STRING and TIMER databases and R software were adopted in this study. Results: In this study, we demonstrated that the mRNA levels of FAM83 B/C/D/H were significantly up-regulated in stomach adenocarcinoma (STAD), but the protein level of FAM83G/H were remarkable lowly in STAD. Next, FAM83C/D/G/H were significantly associated with tumor stages in STAD patients. Then, the mutation rate of FAM83 family members in STAD patients was 46%, and the highest mutation rate was FAM83H (23%). Furthermore, the functions of FAM83 family and their 259 co-expression genes were primarily related to Shigellosis, RNA degradation and Ribosome biogenesis in eukaryotes pathway. Besides, we have established the prognostic model of FAM83 family in STAD, including the prognostic model of STAD patients (FAM83C/D/G), STAD with lymph node metastasis (FAM83C/D/G/H) and STAD with ERBB2 high expression (FAM83G/H). FAM83C/D high expression with a poor prognosis, while FAM83G/H high expression with a favorable prognosis of STAD. Additionally, we found that the expression of FAM83C/D/G/H were significantly correlated with the infiltration of six types of immune cells [B cells, CD8+T cells, CD4+T cells, macrophages and Myeloid dendritic cells (DC)], whereas CD4+T cells and Macrophage cells have higher risk scores (HR > 1) when FAM83C lowly expression and FAM83D highly expression. The risk score of NK cells was significantly reduced when FAM83G lowly expression and FAM83H highly expression (HR < 1). Conclusion: These findings suggested that FAM83C/D/G/H might play key roles in STAD tumorigenesis and progression, and FAM83C/D might be risk factors but FAM83G/H might be favorable prognostic factors for STAD patients. In addition, CD4+T cells and Macrophage cells may be the promoters of FAM83D in progression of STAD, while NK cells may promote the protective effect of FAM83H on STAD patients.
ABSTRACT
Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.
