Engagement of programmed death-ligand 1 (PD-L1) with its receptor programmed death 1 (PD-1) on T cells has been speculated to play a major role in suppressing the immune system, which helps tumor cells evade anti-tumor immunity. With the development of whole genome sequencing technologies, microRNAs have gained more attention as an important new layer of molecular regulation. Recent studies have revealed that altered expression of microRNAs play a pivotal role in immune checkpoint and various cellular processes in cancer. In this review, we focused on the latest progress about microRNAs research which involves the regulation of PD-1/PD-L1 immune checkpoint.

Gastric cancer (GC) is a common upper gastrointestinal tumor. Death-associated protein kinase (DAPK1) was found to participate in the development of various malignant tumors. However, there are few reports on DAPK1 in gastric cancer. In this study, the TCGA and GEO datasets were used to explore the expression and role of DAPK1 in gastric cancer. The functions of DAPK1 in gastric cancer were determined by proliferation, migration and invasion assays. In addition, genes co-expressed with DAPK1 in gastric cancer were estimated through the WGCNA and correlation analysis. A DAPK1-related gene prognostic model was constructed using the Cox regression and lasso analyses. The expression of DAPK1 was significantly up-regulated in gastric cancer tissues. Kaplan–Meier analysis showed that low expression of DAPK1 was a favorable prognostic factor of overall survival and disease-free survival for gastric cancer patients. Functional experiments demonstrated that DAPK1 can promote the migration and invasion of gastric cancer cells. WGCNA, correlation analysis, Cox regression, and lasso analyses were applied to construct the DAPK1-related prognostic model. The prognostic value of this prognostic model of DAPK1-related genes was further successfully validated in an independent database. Our results indicated that DAPK1 can promote gastric cancer cell migration and invasion and established four DAPK1-related signature genes for gastric cancer that could independently predict the survival of GC patients.

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.

Background: Osteosarcoma is one of the most malignant tumors, and it occurs mostly in children and adolescents. Currently, surgery and chemotherapy are the main treatments. The recurrence rate is high and the prognosis is often poor. Finding an effective target gene therapy for osteosarcoma may effectively improve its prognosis. Method: In this study, genes essential for the survival of osteosarcoma cells were identified by genome-wide screening of CRISPR-Cas9 based on the DepMap database. The expression of these essential genes in osteosarcoma patients’ tissues and normal tissues was identified in the GSE19276 database. Functional pathway enrichment analysis, protein interaction network construction, and LASSO were performed to construct a prognostic risk model based on these essential genes. CCK8 assay was used to detect the effect of essential gene-LARS (Leucyl-TRNA Synthetase 1) on the proliferation of osteosarcoma. Results: In this study, 785 genes critical for osteosarcoma cell proliferation were identified from the DepMap. Among these 785 essential genes, 59 DEGs were identified in osteosarcoma tissues. In the functional enrichment analysis, these 59 essential genes were mainly enriched in cell cycle-related signaling pathways. Furthermore, we established a risk score module, including LARS and DNAJC17, screened from these 59 genes, and this module could divide osteosarcoma patients into the low-risk and high-risk groups. In addition, knockdown of LARS expression inhibited the proliferative ability of osteosarcoma cells. A significant correlation was found between LARS expression and Monocytic lineage, T cells, and Fibroblasts. Conclusion: In conclusion, LARS was identified as an essential gene for survival in osteosarcoma based on the DepMap database. Knockdown of LARS expression significantly inhibited the proliferation of osteosarcoma cells, suggesting that it is involved in the formation and development of osteosarcoma. The results are useful as a foundation for further studies to elucidate a potential osteosarcoma diagnostic index and therapeutic targets.

Background: Clear cell renal cell carcinoma (ccRCC) accounts for 60-70% of renal cell carcinoma (RCC) cases. Finding more therapeutic targets for advanced ccRCC is an urgent mission. The minichromosome maintenance proteins 2-7 (MCM2-7) protein forms a stable heterohexamer and plays an important role in DNA replication in eukaryotic cells. In the study, we provide a comprehensive study of MCM2-7 genes expression and their potential roles in ccRCC. Methods: The expression and prognosis of the MCM2-7 genes in ccRCC were analyzed using data from TCGA, GEO and ArrayExpress. MCM2-7 related genes were identified by weighted co-expression network analysis (WGCNA) and Metascape. CancerSEA and GSEA were used to analyze the function of MCM2–7 genes in ccRCC. The gene effect scores (CERES) of MCM2-7, which reflects carcinogenic or tumor suppressor, were obtained from DepMap. We used clinical and expression data of MCM2-7 from the TCGA dataset and the LASSO Cox regression analysis to develop a risk score to predict survival of patients with ccRCC. The correlations between risk score and other clinical indicators such as gender, age and stage were also analyzed. Further validation of this risk score was engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Results: The mRNA and protein expression of MCM2-7 were increased in ccRCC compared with normal tissues. High MCM2, MCM4, MCM6 and MCM7 expression were associated with a poor prognosis of ccRCC patients. Functional enrichment analysis revealed that MCM2-7 might influence the progress of ccRCC by regulating the cell cycle. Knockdown of MCM7 can inhibit the proliferation of ccRCC cells. A two-gene risk score including MCM4 and MCM6 can predict overall survival (OS) of ccRCC patients. The risk score was successfully verified by further using Arrayexpress cohort. Conclusion: We analyze MCM2-7 mRNA and protein levels in ccRCC. MCM7 is determined to promote tumor proliferation. Meanwhile, our study has determined a risk score model composed of MCM2-7 can predict the prognosis of ccRCC patients, which may help future treatment strategies.

Gastric cancer peritoneal metastases (GCPM) is a leading cause of GC‐related death. Early detection of GCPM is critical for improving the prognosis of advanced GC. Differentially expressed genes (DEGs) were identified in the GSE62254 database to distinguish between GCPM and non‐GCPM. The gastric cancer peritoneal metastases signature (GCPMs) was developed using DEGs. We analysed the effectiveness of GCPMs as indicators for prognosis, chemotherapy, and immune therapy response in GC patients. Subsequently, we analysed the correlation between GCPMs and immune microenvironment as well as immune escape in GC patients. Random forest model and immunohistochemistry was utilized to identify the crucial genes that can aid in the diagnosis of GCPM. We identified five DEGs and utilized their expression to construct GCPMs. Patients with high GCPMs had a higher likelihood of a poor prognosis, while those with low GCPMs appeared to potentially benefit more from chemotherapy. GCPMs were a dependable marker for predicting the response to immunotherapy. Additionally, GCPMs was found to be significantly linked to stromal score and cancer‐associated fibroblasts. SYNPO2 has been identified as the gene with the highest significance in the diagnosis of GCPM. Immunohistochemistry suggests that SYNPO2‐positive expression in tumour cells, fibroblasts, inflammatory cell may be associated with promoting peritoneal metastasis in GC. GCPMs have shown to be a promising biomarker for predicting the prognosis and response of GC patients to chemotherapy and immunotherapy. The use of GCPMs for individual tumour evaluation may pave the way for personalized treatment for GC patients in the future.