Transcriptional expressions of hsa-mir-183 predicted target genes as independent indicators for prognosis in bladder urothelial carcinoma

Multifaceted roles of PDCD6 both within and outside the cell

Programmed cell death protein 6 (PDCD6) is an evolutionarily conserved Ca2+-binding protein. PDCD6 is involved in regulating multifaceted and pleiotropic cellular processes in different cellular compartments. For instance, nuclear PDCD6 regulates apoptosis and alternative splicing. PDCD6 is required for coat protein complex II-dependent endoplasmic reticulum-to-Golgi apparatus vesicular transport in the cytoplasm. Recent advances suggest that cytoplasmic PDCD6 is involved in the regulation of cytoskeletal dynamics and innate immune responses. Additionally, membranous PDCD6 participates in membrane repair through endosomal sorting complex required for transport complex-dependent membrane budding. Interestingly, extracellular vesicles are rich in PDCD6. Moreover, abnormal expression of PDCD6 is closely associated with many diseases, especially cancer. PDCD6 is therefore a multifaceted but pivotal protein in vivo. To gain a more comprehensive understanding of PDCD6 functions and to focus and stimulate PDCD6 research, this review summarizes key developments in its role in different subcellular compartments, processes, and pathologies.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

Comprehensive Molecular Analyses of an SLC Family-Based Model in Stomach Adenocarcinoma

Background: Solute carrier (SLC) family members are crucial in transporting amino acids across membranes. Amino acids are indispensable for both cancer and immune cells. However, the clinical significance of amino acid transporting SLC members in stomach adenocarcinoma (STAD) remains unclear. This study aimed to develop an SLC family-based model to predict the prognosis and the response of STAD patients to immunotherapy.

Methods: A total of 1239 tumor cases were obtained from online databases. The training set (n = 371) consisted of RNA sequencing profiles obtained from The Cancer Genome Atlas (TCGA), while those from Gene Expression Omnibus (GEO) were used as the test set. Subsequently, the clinical characteristics and immune profiles were investigated, and potential immunotherapy response prediction values of the model were assessed.

Results: Based on the TCGA cohort, an SLC family-based model was developed using multivariate Cox analysis. All tumor cases were stratified into high- and low-risk groups considering the SLC model. High-risk patients had a worse overall survival (OS) than low-risk patients, consistent with the results of GEO cohorts. Comprehensive analyses revealed that the high-risk group was correlated with aggressiveness-related pathways, whereas the low-risk group had better T helper cell infiltration and stronger immunotherapy response. Compared to the high-risk group, the low-risk group presented increased PD-L1 and tumor mutation burden.

Conclusion: This SLC family-based model has the potential to predict the prognosis and immunotherapy outcomes of STAD patients. The survival of patients in the low-risk group was greatly prolonged, and the patients may benefit more from immunotherapy.