High-temperature stress can cause serious abiotic damage that limits the yield and quality of rice. Heat tolerance (HT) during the flowering stage of rice is a key trait that can guarantee a high and stable yield under heat stress. HT is a complex trait that is regulated by multiple quantitative trait loci (QTLs); however, few underlying genes have been fine mapped and cloned. In this study, the F2:3 population derived from a cross between Huanghuazhan (HHZ), a heat-tolerant cultivar, and 9311, a heat-sensitive variety, was used to map HT QTLs during the flowering stage in rice. A new major QTL, qHTT8, controlling HT was identified on chromosome 8 using the bulked-segregant analysis (BSA)-seq method. The QTL qHTT8 was mapped into the 3,555,000-4,520,000 bp, which had a size of 0.965 Mb. The candidate region of qHTT8 on chromosome 8 contained 65 predicted genes, and 10 putative predicted genes were found to be associated with abiotic stress tolerance. Furthermore, qRT-PCR was performed to analyze the differential expression of these 10 genes between HHZ and 9311 under high temperature conditions. LOC_Os08g07010 and LOC_Os08g07440 were highly induced in HHZ compared with 9311 under heat stress. Orthologous genes of LOC_Os08g07010 and LOC_Os08g07440 in plants played a role in abiotic stress, suggesting that they may be the candidate genes of qHTT8. Generally, the results of this study will prove useful for future efforts to clone qHTT8 and breed heat-tolerant varieties of rice using marker-assisted selection.

In recent years, the relationship between Cyp2C19*2 gene polymorphism and clopidogrel resistance reflected by platelet function assay has been studied extensively, but there is no clear conclusion yet. In order to evaluate the relationship between Cyp2C19*2 gene polymorphism and clopidogrel resistance more accurately, meta-analysis was conducted in this study. The I2 value taking 50% as the limit, the heterogeneity is judged as high or low, and then a random effect model or a fixed effect model is selected for statistical analysis. PubMed, EMBASE, Web of Science, CNKI, and China Wanfang database were searched, and the related literatures from the establishment of the database to May 2020 were collected and analyzed by STATA 15.0 software. A total of 3,073 patients were involved in 12 studies, including 1,174 patients with clopidogrel resistance and 1,899 patients with non-clopidogrel resistance. The results of this study showed that allele model (A vs. G): OR = 2.42 (95%CI: 1.97-2.98); dominant model (AA+GA vs. GG): OR = 2.74 (95%CI: 2.09-3.59); recessive model (AA vs. GA+GG): OR = 4.07 (95%CI: 3.06-5.41); homozygous model (AA vs. GG): OR = 5.70 (95%CI: 4.22-7.71); heterozygote model (GA vs. GG): OR = 2.32 (95%CI: 1.76-3.07), the differences were statistically significant. Also, the analysis of the Ethnicity subgroup indicated that the Asian allele model and the other four gene models were statistically significant. In conclusion, Cyp2C19*2 gene polymorphism is strongly associated with clopidogrel resistance. Allele A, genotype GA, AA, and GG + GA can increase clopidogrel resistance, especially in the Asian population.

Clear cell renal cell carcinoma (ccRCC) is essentially a metabolic disorder characterized by reprogramming of several metabolic pathways. Acyl-coenzyme A thioesterases (ACOTs) are critical enzymes involved in fatty acid metabolism; however, the roles of ACOTs in ccRCC remain unclear. This study explored ACOTs expressions and their diagnostic and prognostic values in ccRCC.

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Bladder cancer (BC) is the most common tumor of the urinary system. Non-muscle-invasive bladder cancer (NMIBC) has a high recurrence rate after surgery, and patients with muscle-invasive bladder cancer (MIBC) have poor quality of life after radical surgery. Understanding the molecular mechanism of bladder cancer is helpful for providing a more appropriate treatment approach. Annexins are calcium-binding proteins and play an important role in different tumor cells. However, the role of the annexin family in bladder cancer has not been studied in detail.

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients' deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.

Phosphoserine aminotransferase 1 (PSAT1) may be an oncogene that plays an important role in various cancer types. However, there are still many gaps in the expression of PSAT1 gene and its biological impact in different types of tumors. Here, we performed an integrated pan-cancer analysis to explore the potential molecular mechanisms of PSAT1 in cancers. We found that most human tumors express higher levels of PSAT1 than normal tissues, and that higher PSAT1 expression is associated with worse prognosis in Lung adenocarcinoma (LUAD), Pan-kidney cohort (KIPAN) and breast invasive carcinoma (BRCA), etc. In BRCA cases, the prognosis of patients with altered PSAT1 was worse than that of patients without alteration. In addition, PSAT1 hypermethylation is associated with T cell dysfunction and shortened survival time in BRCA. The Gene Set Enrichment Analysis (GSEA) analysis showed that PSAT1 can be enriched into the classic signaling pathways of cancer such as mTORC1 signaling, MYC targets and JAK STAT3. Further analysis demonstrated that PSAT1 was enriched in immune related signaling pathways in LUAD and BRCA. The results of immunoassay showed that PSAT1 was associated with immune cell infiltration in multiple cancer species. Furthermore, expression of PSAT1 was correlated with both tumor mutational burden (TMB) and microsatellite instability (MSI) in BRCA. Additionally, a remarkable correlation was found between PSAT1 expression and TMB in LUAD, and the expression of PSAT1 was negatively correlated with the Tumor Immune Dysfunction and Exclusion (TIDE) value, suggesting a good effect of immunotherapy. Together, these data suggest that PSAT1 expression is associated with the clinical prognosis, DNA methylation, gene mutations, and immune cell infiltration, contributing to clarify the role of PSAT1 in tumorigenesis from a variety of perspectives. What's more, PSAT1 may be a new biomarker for survival and predicting the efficacy of immunotherapy for LUAD and BRCA.

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes' methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Tumor progression includes the obtainment of progenitor and stem cell-like features and the gradual loss of a differentiated phenotype. Stemness was defined as the potential for differentiation and self-renewal from the cell of origin. Previous studies have confirmed the effective application of stemness in a number of malignancies. However, the mechanisms underlying the growth and maintenance of multiple myeloma (MM) stem cells remain unclear. We calculated the stemness index for samples of MM by utilizing a novel one-class logistic regression (OCLR) machine learning algorithm and found that mRNA expression-based stemness index (mRNAsi) was an independent prognostic factor of MM. Based on the same cutoff value, mRNAsi could stratify MM patients into low and high groups with different outcomes. We identified 127 stemness-related signatures using weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Functional annotation and pathway enrichment analysis indicated that these genes were mainly involved in the cell cycle, cell differentiation, and DNA replication and repair. Using the molecular complex detection (MCODE) algorithm, we identified 34 pivotal signatures. Meanwhile, we conducted unsupervised clustering and classified the MM cohorts into three MM stemness (MMS) clusters with distinct prognoses. Samples in MMS-cluster3 possessed the highest stemness fractions and the worst prognosis. Additionally, we applied the ESTIMATE algorithm to infer differential immune infiltration among the three MMS clusters. The immune core and stromal score were significantly lower in MMS-cluster3 than in the other clusters, supporting the negative relation between stemness and anticancer immunity. Finally, we proposed a prognostic nomogram that allows for individualized assessment of the 3- and 5-year overall survival (OS) probabilities among patients with MM. Our study comprehensively assessed the MM stemness index based on large cohorts and built a 34-gene based classifier for predicting prognosis and potential strategies for stemness treatment.

Purpose: The aim of this study was to conduct a comprehensive transcriptomic analysis to explore the potential biological functions of noncoding RNA (ncRNAs) in temporomandibular joint osteoarthritis (TMJOA). Methods: Whole transcriptome sequencing was performed to identify differentially expressed genes (DEGs) profiles between the TMJOA and normal groups. The functions and pathways of the DEGs were analyzed using Metascape, and a competitive endogenous RNA (ceRNA) network was constructed using Cytoscape software. Results: A total of 137 DEmRNAs, 65 DEmiRNAs, 132 DElncRNAs, and 29 DEcircRNAs were identified between the TMJOA and normal groups. Functional annotation of the DEmRNAs revealed that immune response and apoptosis are closely related to TMJOA and also suggested key signaling pathways related to TMJOA, including chronic depression and PPAR signaling pathways. We identified vital mRNAs, including Klrk1, Adipoq, Cryab, and Hspa1b. Notably, Adipoq expression in cartilage was significantly upregulated in TMJOA compared with normal groups (10-fold, p < 0.001). According to the functional analysis of DEmRNAs regulated by the ceRNA network, we found that ncRNAs are involved in the regulation of autophagy and apoptosis. In addition, significantly DEncRNAs (lncRNA-COX7A1, lncRNA-CHTOP, lncRNA-UFM1, ciRNA166 and circRNA1531) were verified, and among these, circRNA1531 (14.5-fold, p < 0.001) and lncRNA-CHTOP (14.8-fold, p < 0.001) were the most significantly downregulated ncRNAs. Conclusion: This study showed the potential of lncRNAs, circRNAs, miRNAs, and mRNAs may as clinical biomarkers and provides transcriptomic insights into their functional roles in TMJOA. This study identified the transcriptomic signatures of mRNAs associated with immunity and apoptosis and the signatures of ncRNAs associated with autophagy and apoptosis and provides insight into ncRNAs in TMJOA.