Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

The main purpose of this study was to understand the type of dairy cattle that can be optimally used by smallholder farmers in various production environments such that they will maximize their yields without increasing the level of inputs. Anecdotal evidence and previous research suggests that the optimal level of taurine inheritance in crossbred animals lies between 50 and 75% when considering total productivity in tropical management clusters. We set out to assess the relationship between breed composition and productivity for various smallholder production systems in Tanzania. We surveyed 654 smallholder dairy households over a 1-year period and grouped them into production clusters. Based on supplementary feeding, milk productivity and sale as well as household wealth status four clusters were described: low-feed-low-output subsistence, medium-feed-low-output subsistence, maize germ intensive semi-commercial and feed intensive commercial management clusters. About 839 crossbred cows were genotyped at approximately 150,000 single nucleotide polymorphism (SNP) loci and their breed composition determined. Percentage dairyness (proportion of genes from international dairy breeds) was estimated through admixture analysis with Holstein, Friesian, Norwegian Red, Jersey, Guernsey, N'Dama, Gir, and Zebu as references. Four breed types were defined as RED-GUE (Norwegian Red/Friesian-Guernsey; Norwegian Red/Friesian-Jersey), RED-HOL (Norwegian Red/Friesian-Holstein), RED-Zebu (Norwegian Red/Friesian-Zebu), Zebu-RED (Zebu-Norwegian Red/Friesian) based on the combination of breeds that make up the top 76% breed composition. A fixed regression model using a genomic kinship matrix was used to analyze milk yield records. The fitted model accounted for year-month-test-date, parity, age, breed type and the production clusters as fixed effects in the model in addition to random effects of animal and permanent environment effect. Results suggested that RED-Zebu breed type with dairyness between 75 and 85% is the most appropriate for a majority of smallholder management clusters. Additionally, for farmers in the feed intensive management group, animals with a Holstein genetic background with at least 75% dairy composition were the best performing. These results indicate that matching breed type to production management group is central to maximizing productivity in smallholder systems. The findings from this study can serve as a basis to inform the development of the dairy sector in Tanzania and beyond.

Understanding the biological mechanisms of climatic adaptation is of paramount importance for the optimization of breeding programs and conservation of genetic resources. The aim of this study was to investigate genetic diversity and unravel genomic regions potentially under selection for heat and/or cold tolerance in thirty-two worldwide cattle breeds, with a focus on Chinese local cattle breeds adapted to divergent climatic conditions, Datong yak (Bos grunniens; YAK), and Bali (Bos javanicus) based on dense SNP data. In general, moderate genetic diversity levels were observed in most cattle populations. The proportion of polymorphic SNP ranged from 0.197 (YAK) to 0.992 (Mongolian cattle). Observed and expected heterozygosity ranged from 0.023 (YAK) to 0.366 (Sanhe cattle; SH), and from 0.021 (YAK) to 0.358 (SH), respectively. The overall average inbreeding (±SD) was: 0.118 ± 0.028, 0.228 ± 0.059, 0.194 ± 0.041, and 0.021 ± 0.004 based on the observed versus expected number of homozygous genotypes, excess of homozygosity, correlation between uniting gametes, and runs of homozygosity (ROH), respectively. Signatures of selection based on multiple scenarios and methods (F ST, HapFLK, and ROH) revealed important genomic regions and candidate genes. The candidate genes identified are related to various biological processes and pathways such as heat-shock proteins, oxygen transport, anatomical traits, mitochondrial DNA maintenance, metabolic activity, feed intake, carcass conformation, fertility, and reproduction. This highlights the large number of biological processes involved in thermal tolerance and thus, the polygenic nature of climatic resilience. A comprehensive description of genetic diversity measures in Chinese cattle and YAK was carried out and compared to 24 worldwide cattle breeds to avoid potential biases. Numerous genomic regions under positive selection were detected using three signature of selection methods and candidate genes potentially under positive selection were identified. Enriched function analyses pinpointed important biological pathways, molecular function and cellular components, which contribute to a better understanding of the biological mechanisms underlying thermal tolerance in cattle. Based on the large number of genomic regions identified, thermal tolerance has a complex polygenic inheritance nature, which was expected considering the various mechanisms involved in thermal stress response.

Osteoarthritis (OA) is one of the most common age-related degenerative diseases. In recent years, some studies have shown that pathological changes in the synovial membrane occur earlier than those in the cartilage in OA. However, the molecular mechanism of synovitis in the pathological process of OA has not been elucidated. This study aimed to identify novel biomarkers associated with OA and to emphasize the role of immune cells in the pathogenesis of OA.

Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λ k , which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λ k , after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global-local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global-local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the "U" type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global-local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.

Background: The mitochondrial-associated protein leucine-rich pentatricopeptide repeat-containing (LRPPRC) exerts multiple functions involved in physiological processes, including mitochondrial gene translation, cell cycle progression, and tumorigenesis. Previously, LRPPRC was reported to regulate mitophagy by interacting with Bcl-2 and Beclin-1 and thus modifying the activation of PI3KCIII and autophagy. Considering that LRPPRC was found to be negatively associated with survival rate, we hypothesize that LRPPRC may be involved in pancreatic cancer progression via its regulation of autophagy. Methods: Real-time quantitative polymerase chain reaction was performed to detect the expression of LRPPRC in 90 paired pancreatic cancer and adjacent tissues and five pancreatic cancer cell lines. Mitochondrial reactive oxidative species level and function were measured. Mitophagy was measured by performing to detect LC3 levels. Results: By performing a real-time quantitative polymerase chain reaction, the association of LRPPRC with the prognosis of pancreatic cancer was established, and pancreatic cancer tissues had significantly higher LRPPRC expression than adjacent tissues. LRPPRC was negatively associated with the overall survival rate. LRPPRC was also upregulated in pancreatic cancer cell lines. Knockdown of LRPPRC promoted reactive oxidative species accumulation, decreased mitochondrial membrane potential, promoted autophagy/mitophagy, and induced mitochondrial dysfunction. Subsequently, knockdown of LRPPRC inhibited malignant behaviors in PANC-1 cells, including proliferation, migration, invasion, tumor formation, and chemoresistance to gemcitabine. Finally, by inhibiting autophagy/mitophagy using 3-MA, the inhibitory effect of LRPPRC knockdown on proliferation was reversed. Conclusion: Taken together, our results indicate that LRPPRC may act as an oncogene via maintaining mitochondrial homeostasis and could be used as a predictive marker for patient prognosis in pancreatic cancer.

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.

Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created "Analysis of Breast Cancer GWAS" (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are involved in the mechanism underlying cerebral dysfunction after deep hypothermic circulatory arrest (DHCA), although the exact details have not been elucidated. To explore the expression profiles of lncRNAs and miRNAs in DHCA cerebral injury, we determined the lncRNA, miRNA and mRNA expression profiles in the cerebral cortex of DHCA and sham rats. First, a rat model of DHCA was established, and high-throughput sequencing was performed to analyze the differentially expressed RNAs (DERNAs). Then, the principal functions of the significantly deregulated genes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Expression networks (lncRNAs-miRNAs-mRNAs and transcription factors (TFs)-miRNAs-mRNAs) were also established. Finally, the expression of DERNAs was confirmed by quantitative real-time PCR (RT-qPCR). We identified 89 lncRNAs, 45 miRNAs and 59 mRNAs between the DHCA and sham groups and constructed a comprehensive competitive endogenous RNAs (ceRNAs) network. A TF-miRNA-mRNA regulatory network was also established. Finally, we predicted that Lcorl-miR-200a-3p-Ttr, BRD4-Ccl2 and Ep300-miR-200b-3p-Tmem72 may participate in the pathogenesis of DHCA cerebral injury.

Multiple studies have investigated selection signatures in domestic cattle and other species. However, there is a dearth of information about the response to selection in genomes of highly admixed crossbred cattle in relation to production and adaptation to tropical environments. In this study, we evaluated 839 admixed crossbred cows sampled from two major dairy regions in Tanzania namely Rungwe and Lushoto districts, in order to understand their genetic architecture and detect genomic regions showing preferential selection. Animals were genotyped at 150,000 SNP loci using the Geneseek Genomic Profiler (GGP) High Density (HD) SNP array. Population structure analysis showed a large within-population genetic diversity in the study animals with a high degree of variation in admixture ranging between 7 and 100% taurine genes (dairyness) of mostly Holstein and Friesian ancestry. We explored evidence of selection signatures using three statistical methods (iHS, XP-EHH, and pcadapt). Selection signature analysis identified 108 candidate selection regions in the study population. Annotation of these regions yielded interesting genes potentially under strong positive selection including ABCG2, ABCC2, XKR4, LYN, TGS1, TOX, HERC6, KIT, PLAG1, CHCHD7, NCAPG, and LCORL that are involved in multiple biological pathways underlying production and adaptation processes. Several candidate selection regions showed an excess of African taurine ancestral allele dosage. Our results provide further useful insight into potential selective sweeps in the genome of admixed cattle with possible adaptive and productive importance. Further investigations will be necessary to better characterize these candidate regions with respect to their functional significance to tropical adaptations for dairy cattle.

Genome-wide association studies (GWAS) have hitherto identified several germline variants associated with cancer susceptibility, but the molecular functions of these risk modulators remain largely uncharacterized. Recent studies have begun to uncover the regulatory potential of noncoding GWAS SNPs using epigenetic information in corresponding cancer cell types and matched normal tissues. However, this approach does not explore the potential effect of risk germline variants on other important cell types that constitute the microenvironment of tumor or its precursor. This paper presents evidence that the breast-cancer-associated variant rs3903072 may regulate the expression of CTSW in tumor-infiltrating lymphocytes. CTSW is a candidate tumor-suppressor gene, with expression highly specific to immune cells and also positively correlated with breast cancer patient survival. Integrative analyses suggest a putative causative variant in a GWAS-linked enhancer in lymphocytes that loops to the 3' end of CTSW through three-dimensional chromatin interaction. Our work thus poses the possibility that a cancer-associated genetic variant could regulate a gene not only in the cell of cancer origin but also in immune cells in the microenvironment, thereby modulating the immune surveillance by T lymphocytes and natural killer cells and affecting the clearing of early cancer initiating cells.

Long non-coding RNA (LncRNA) has high expression in the brain. Animal studies have shown that lncRNA plays an important role in brain functions and mediates the development of many neurological diseases. However, data on the expression of lncRNAs and the clinical significance in prematurely born infants with diseases such as periventricular white matter damage (PWMD) remains scant. Here, we compared the expression of the lncRNAs in whole blood samples obtained from prematurely born infants with PWMD with samples from prematurely born infants without PWMD. Our data demonstrated differential expression of the lncRNAs between the two groups. Further, we showed that the lncRNAs play important roles in the development of PWMD. Our findings give insights into the functions of the lncRNAs in PWMD and provide evidence for the improvement of diagnostic and treatment strategies in infants with PWMD.

Background: Cholangiocarcinoma is characterized by significant cellular heterogeneity and complex intercellular communication, which contribute to its progression and therapeutic resistance. Therefore, unraveling this complexity is essential for the development of effective treatments. Methods: We employed single-cell RNA sequencing (scRNA-seq) to investigate cellular heterogeneity and intercellular communication in cholangiocarcinoma and adjacent normal tissues from two patients. Distinct cell types were identified, and gene ontology analyses were conducted to determine enriched pathways. Moreover, cell-cell communications were analyzed using CellChat, a computational framework. Additionally, we performed sub-clustering analysis of T cells and fibroblasts. Results: The scRNA-seq analysis revealed distinct cell clusters and diverse cellular compositions of cholangiocarcinoma. CellChat analysis underscored an amplified outgoing signal from fibroblasts within the tumor, suggesting their pivotal role in the tumor microenvironment. Furthermore, T cell sub-clustering analysis revealed an active immune response within the tumor and new tumor-specific T cell clonotypes, suggesting scope for targeted immunotherapies. Moreover, fibroblast sub-clustering analysis indicated distinct functional states and highlighted the role of activated fibroblasts in shaping intercellular communication, particularly via CD99 and FN1 signaling. Conclusion: Our findings reveal the intricate cellular heterogeneity and dynamic intercellular communication in cholangiocarcinoma, providing valuable insights into disease progression and potential therapeutic strategies.

Glutathione peroxidase 8 (GPX8), located in the endoplasmic reticulum, is associated with poor prognosis in several cancers. However, the expression and functions of GPX8 in cancers remain unclear. The purpose of this study was to explore the expression and functions of GPX8 in glioblastoma (GBM). We obtained expression data of GPX8 by accessing the TCGA, CGGA, GEPIA, and TIMER2.0 databases and validated them using western blot and immunohistochemistry. The Kaplan-Meier overall survival curve and Cox regression model were used to evaluate the prognostic value of GPX8 in glioma patients. Gene ontology (GO) and function enrichment analysis were used to investigate the potential function of GPX8 in GBM. Correlation analysis was used to clarify the role of GPX8 in proneural-mesenchymal transition (PMT). We studied the correlation between GPX8 expression and GBM immune infiltration by accessing cBioPortal and TIMER2.0 databases. Here, we demonstrated that GPX8 was significantly upregulated in GBM, and was associated with IDH-wildtype and mesenchymal subtype with poor prognosis. Survival analysis results indicated that GPX8 is an independent prognostic factor for overall survival (OS) in all WHO-grade glioma patients. Through the functional studies, we found that high expression of GPX8 correlated with mesenchymal signature and negatively correlated with proneural signature, indicating that GPX8 might promote PMT in GBM. Finally, based on correlation analysis, we found that the expression of GPX8 was associated with immune infiltration and the IL1/MYD88/IRAK/NF-κB pathway in GBM. Our results show that GPX8 is a key factor affecting the prognosis of GBM patients, and its targeting has the potential to provide a novel therapeutic approach.

It has been proposed that immune abnormalities may be implicated with pathophysiology of schizophrenia. The nod-like receptor pyrin domain-contraining protein 3 (NLRP3) can trigger immune-inflammatory cascade reactions. In this study, we intended to identify the role of gene encoding NLRP3 (NLRP3) in susceptibility to schizophrenia and its clinical features. For the NLRP3 mRNA expression analysis, 53 drug-naïve patients with first-episode schizophrenia and 56 healthy controls were enrolled. For the genetic study, a total of 823 schizophrenia patients and 859 controls were recruited. Among them, 239 drug-naïve patients with first-episode schizophrenia were enrolled for clinical evaluation. There is no significant difference in NLRP3 mRNA levels between patients with schizophrenia and healthy controls (p = 0.07). We did not observe any significant differences in allele and genotype frequencies of rs10754558 polymorphism between the schizophrenia and control groups. We noticed significant differences in the scores of RBANS attention and total scores between the patients with different genotypes of rs10754558 polymorphism (p = 0.001 and p < 0.01, respectively). Further eQTL analysis presented a significant association between the rs10754558 polymorphism and NLRP3 in frontal cortex (p = 0.0028, p = 0.028 after Bonferroni correction). Although our findings did not support NLRP3 confer susceptibility to schizophrenia, NLRP3 may be a risk factor for cognitive impairment, especially attention deficit in this disorder.

A cohort of 542 individuals in 166 families with congenital hearing loss was recruited for whole-exome sequencing analysis. Here, we report the identification of three variants in five affected individuals in two unrelated families. In family 1, a nonsense mutation (c.1516C>T, p.R506*) in the ATP6V1B2 gene, a known causal allele for dominant deafness-onychodystrophy (DDOD), was identified in the mother and son with DDOD. However, a novel heterozygous variant (c.1590T>G, p.D530E) in TJP2, a known causal gene for hearing-loss, was also detected in the patients. In family 2, the same mutation (c.1516C>T, p.R506*) of ATP6V1B2 was detected from the father and daughter with DDOD. Furthermore, a novel heterozygous variant (c.733A>G, p.M245V) in the KIF11 gene was identified from the spouse with sensorineural hearing-loss and epilepsy. Notably, genotype-phenotype analysis of KIF11-associated disorders revealed that the p.M245V and two reported hearing-loss-associated variants (p.S235C and p.H244Y) are all mapped to a single β-sheet (Ser235∼M245) in the kinesin motor domain. Together, this is the first demonstration that ATP6V1B2-caused DDOD is an autosomal dominant genetic disease, compared to previous cases with de novo mutation. Our findings expand the variant spectrum of hearing-loss-associated genes and provide new insights on understanding of hearing-loss candidate genes ATP6V1B2, TJP2, and KIF11.

The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients' OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.

Primary familial brain calcification (PFBC, OMIM#213600), also known as Fahr's disease, is a rare autosomal dominant or recessive neurodegenerative disorder characterized by bilateral and symmetrical microvascular calcifications affecting multiple brain regions, particularly the basal ganglia (globus pallidus, caudate nucleus, and putamen) and thalamus. The most common clinical manifestations include cognitive impairment, neuropsychiatric signs, and movement disorders. Loss-of-function mutations in SLC20A2 are the major genetic causes of PFBC.

Alternative polyadenylation (APA) is a post-translational modification that occurs during mRNA maturation in humans. Studies suggested that abnormal APA events are associated with the genesis and progression of malignant tumors. Here, we aimed to comprehensively evaluate the prognostic value of APA events involved in breast cancer (BC). Both APA events and clinical information for BC patients were downloaded from The Cancer Genome Atlas (TCGA) database to identify prognosis-related APA events in BC. A total of 462 APA events and 374 APA events were shown to be significantly related to overall survival (OS) and relapse-free survival (RFS), respectively, of BC patients. The TCGA set was randomly divided into a training and a test set. Key prognosis-related APA events were selected by LASSO regression to build prediction signatures for OS and RFS by multivariate Cox regression analysis in the training, test, and whole set. BC patients were stratified into high-risk and low-risk groups based on median risk scores. Kaplan-Meier survival analysis demonstrated that low-risk groups had better OS and RFS than high-risk groups in all three sets. The time-dependent receiver operating characteristic (ROC) curves showed that our signatures had a good predictive ability for survival and recurrence for BC patients in all three sets. The independent prognostic indicators-based nomogram model had excellent performance and considerable net benefit for predicting the OS and RFS in BC. A PPI network was constructed between key prognosis and core regulators associated with APA, consisting of 48 nodes and 244 edges. Functional enrichment analysis also revealed their association with RNA processing and RNA synthesis. Collectively, our data indicate that prognostic signatures based on APA events may be powerful prognostic predictors for OS and RFS in BC.

Depressive symptoms could be considered a mutual manifestation of major depressive disorder and schizophrenia. Rs3758391 is a functional locus of Sirtuin (SIRT1) involving depression etiology. In this study, we hypothesized that the SIRT1 SNP rs3758391 might be a hazard for schizophrenia pathogenesis, especially related to the appearance of depressive symptoms. We recruited 723 healthy controls and 715 schizophrenia patients, the occurrence of psychotic and depressive symptoms was evaluated by Calgary Depression Scale (CDSS) and PANSS. Meanwhile, qt-PCR was used to detect the mRNA levels of SIRT1 in peripheral blood of 197 olanzapine monotherapy schizophrenia patients. 45.6% of schizophrenia patients had depressive symptoms. In the patient group, mRNA levels of patients with depressive symptoms were significantly lower than those without depressive symptoms (P < 0.01). CDSS scores of schizophrenia patients with different rs3758391 genotypes were significantly different (P < 0.01). Post hoc comparisons indicated that the CDSS scores of rs3758391 C/C and C/T carriers were higher than those of T/T carriers (Ps < 0.01). In the occipital cortex, our eQTL analysis showed that there was a clear correlation between rs3758391 and the SIRT1 mRNA levels. Our preliminary findings provide suggestive evidence that SIRT1 makes schizophrenia patients more prone to depressive symptoms. This SNP might be a biomarker of depression in schizophrenia.