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Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/.Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

Sorghum [Sorghum bicolor (L.) Moench] is one of the most important cereal crops grown worldwide but is often attacked by greenbug (aphid). In response to aphid attack, host plant initiates a large transcriptional reorganization, leading to activation of the host defense genes in aphid-attacked plants. In this study, our objective was to analyze defensive responses of sorghum against aphid and identify aphid resistance genes in sorghum. For the experiments, seedlings developed from an aphid resistant germplasm line (PI 550607) were divided into two groups, then, one group was infested with greenbug ((Schizaphis graminum Rondani) and the other group was used as control (un-infested). In addition, seedlings of sorghum cultivar Tx 7000, a susceptible genotype, prepared under the same conditions, were used as a genetic control. Those plant samples were used to develop transcriptional profiles using the microarray method, from which 26.1% of the 1,761 cDNA sequences spotted on the microarray showed altered expression between two treatments at 4 days after infestation. Sequence annotation and molecular analysis revealed that many differentially expressed genes (DEGs) were related to direct host defense or signal transduction pathways, which regulate host defense. In addition to common responsive genes, unique transcripts were identified in response to greenbug infestation specifically. Later, a similar transcriptional profiling was conducted using the RNA-seq method, resulted in the identification of 2,856 DEGs in the resistant line with a comparison between infested and non-infested at 4 days and 4,354 DEGs in the resistant genotype compared to the susceptible genotype at 4 days. Based on the comparative analysis, the data of RNA-seq provided a support for the results from the microarray study as it was noticed that many of the DEGs are common in both platforms. Analysis of the two differential expression profiles indicate that aphid triggered dynamic defense responses in sorghum plants and sorghum plant defense against aphid is a complex process involving both general defense systems and specific resistance mechanisms. Finally, the results of the study provide new insights into the mechanisms underlying host plant defense against aphids and will help us design better strategies for effectively controlling aphid pest.

Background and objectives: Mechanistic research suggests synergistic effects of cardiovascular disease (CVD) and dementia pathologies on cognitive decline. Interventions targeting proteins relevant to shared mechanisms underlying CVD and dementia could also be used for the prevention of cognitive impairment.Methods: We applied Mendelian randomisation (MR) and colocalization analysis to investigate the causal relationships of 90 CVD-related proteins measured by the Olink CVD I panel with cognitive traits. Genetic instruments for circulatory protein concentrations were obtained using a meta-analysis of genome-wide association studies (GWAS) from the SCALLOP consortium (N = 17,747) based on three sets of criteria: 1) protein quantitative trait loci (pQTL); 2) cis-pQTL (pQTL within ±500 kb from the coding gene); and 3) brain-specific cis-expression QTL (cis-eQTL) which accounts for coding gene expression based on GTEx8. Genetic associations of cognitive performance were obtained from GWAS for either: 1) general cognitive function constructed using Principal Component Analysis (N = 300,486); or, 2) g Factor constructed using genomic structural equation modelling (N = 11,263–331,679). Findings for candidate causal proteins were replicated using a separate protein GWAS in Icelanders (N = 35,559).Results: A higher concentration of genetically predicted circulatory myeloperoxidase (MPO) was nominally associated with better cognitive performance (p < 0.05) using different selection criteria for genetic instruments. Particularly, brain-specific cis-eQTL predicted MPO, which accounts for protein-coding gene expression in brain tissues, was associated with general cognitive function (βWald = 0.22, PWald = 2.4 × 10−4). The posterior probability for colocalization (PP.H4) of MPO pQTL with the g Factor was 0.577. Findings for MPO were replicated using the Icelandic GWAS. Although we did not find evidence for colocalization, we found that higher genetically predicted concentrations of cathepsin D and CD40 were associated with better cognitive performance and a higher genetically predicted concentration of CSF-1 was associated with poorer cognitive performance.Conclusion: We conclude that these proteins are involved in shared pathways between CVD and those for cognitive reserve or affecting cognitive decline, suggesting therapeutic targets able to reduce genetic risks conferred by cardiovascular disease.

Background: Karyopherin alpha (KPNA), a nuclear transporter, has been implicated in the development as well as the progression of many types of malignancies. Immune homeostasis is a multilevel system which regulated by multiple factors. However, the functional significance of the KPNA family in the pathogenesis of lung adenocarcinoma (LUAD) and the impact of immune homeostasis are not well characterized.Methods: In this study, by integrating the TCGA-LUAD database and Masked Somatic Mutation, we first conducted an investigation on the expression levels and mutation status of the KPNA family in patients with LUAD. Then, we constructed a prognostic model based on clinical features and the expression of the KPNA family. We performed functional enrichment analysis and constructed a regulatory network utilizing the differential genes in high-and low-risk groups. Lastly, we performed immune infiltration analysis using CIBERSORT.Results: Analysis of TCGA datasets revealed differential expression of the KPNA family in LUAD. Kaplan-Meier survival analyses indicated that the high expression of KPNA2 and KPNA4 were predictive of inferior overall survival (OS). In addition, we constructed a prognostic model incorporating clinical factors and the expression level of KPNA4 and KPNA5, which accurately predicted 1-year, 3-years, and 5-years survival outcomes. Patients in the high-risk group showed a poor prognosis. Functional enrichment analysis exhibited remarkable enrichment of transcriptional dysregulation in the high-risk group. On the other hand, gene set enrichment analysis (GSEA) displayed enrichment of cell cycle checkpoints as well as cell cycle mitotic in the high-risk group. Finally, analysis of immune infiltration revealed significant differences between the high-and low-risk groups. Further, the high-risk group was more prone to immune evasion while the inflammatory response was strongly associated with the low-risk group.Conclusions: the KPNA family-based prognostic model reflects many biological aspects of LUAD and provides potential targets for precision therapy in LUAD.

Background: Atopic dermatitis (AD) is the most common chronic skin inflammatory disease. Prior observational studies have reported inconsistent results on the association of AD with ischemic stroke and coronary heart disease. In this study, we applied two-sample Mendelian randomization (MR) to evaluate the causal effect of AD on ischemic stroke and coronary heart disease.Methods: Twelve single-nucleotide polymorphisms robustly associated with AD (p < 5 × 10–8) were obtained from a genome-wide association study that included 10,788 cases and 30,047 controls by the EArly Genetics and Life course Epidemiology (EAGLE) Consortium (excluding the 23andMe study). The corresponding data for ischemic stroke (34,217 cases and 406,111 controls), large artery stroke (4,373 cases and 406,111 controls), cardioembolic stroke (7,193 cases and 406,111 controls), small vessel stroke (5,386 cases and 192,662 controls), coronary heart disease (122,733 cases and 424,528 controls), and myocardial infarction (43,676 cases and 128,199 controls) were obtained from the MR-Base platform. In the primary MR analyses, we applied the inverse variance weighted method to evaluate the associations. We performed a sensitivity analysis using weighted median, MR-Egger, weighted mode, simple mode, Mendelian Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and leave-one-out methods.Results: In the primary MR analyses, we found no causal association of genetically predicted AD with ischemic stroke [odds ratio (OR) = 1.00, 95% confidence interval (CI): 0.95–1.06], large artery stroke (OR = 1.02, 95% CI: 0.88–1.17), cardioembolic stroke (OR = 1.06, 95% CI: 0.94–1.18), small vessel stroke (OR = 1.05, 95% CI: 0.94–1.17), coronary heart disease (OR = 1.00, 95% CI: 0.94–1.05), and myocardial infarction (OR = 1.03, 95% CI: 0.98–1.09). The results from the primary MR analyses were supported in sensitivity analyses using the weighted median, weighted mode, simple mode, and MR-Egger methods and multivariable MR analyses adjusting for asthma and several traditional risk factors for ischemic stroke and coronary heart disease. MR-Egger intercepts provided no evidence of directional pleiotropy. The MR-PRESSO and leave-one-out analyses did not indicate any outlier instruments.Conclusion: Our MR study does not support a causal association of genetically predicted AD with ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, coronary heart disease, and myocardial infarction.

Angioimmunoblastic T-cell lymphoma (AITL) genomic abnormalities are highly disease-specific, and the ras homology family member A (RHOA) gene is one of the most recurrent mutated genes, especially for RHOA G17V mutation site. Here, we identified a rare RHOA A161E mutation in an AITL patient through gene sequencing platforms. The patient presented with persistent hypereosinophilia, asymptomatic or symptomatic mildly for over 3 years. At diagnosis, this patient manifested night sweats, weight loss, multiple lymphadenopathies, and enlargement of the liver and spleen. We performed a retrospective genetic mutation analysis by whole-exome sequencing (WES) and droplet digital PCR (ddPCR) on serial gastric, intestinal, and lymph node specimens. The genetic mutation testing result demonstrated that a rare RHOA A161E mutation was found, which was elevated significantly on diagnosis related to AITL pathogenesis. Our case confirms that genetic mutation testing is helpful for diagnostic classification in AITL and dynamic monitoring of gene mutations at multiple time points may facilitate early detection of disease diagnosis.

Many studies in recent years have demonstrated that some messenger RNA (mRNA) in platelets can be used as biomarkers for the diagnosis of pan-cancer. The quantitative real-time polymerase chain reaction (RT-qPCR) molecular technique is most commonly used to determine mRNA expression changes in platelets. Accurate and reliable relative RT-qPCR is highly dependent on reliable reference genes. However, there is no study to validate the reference gene in platelets for pan-cancer. Given that the expression of some commonly used reference genes is altered in certain conditions, selecting and verifying the most suitable reference gene for pan-cancer in platelets is necessary to diagnose early stage cancer. This study performed bioinformatics and functional analysis from the RNA-seq of platelets data set (GSE68086). We generated 95 candidate reference genes after the primary bioinformatics step. Seven reference genes (YWHAZ, GNAS, GAPDH, OAZ1, PTMA, B2M, and ACTB) were screened out among the 95 candidate reference genes from the data set of the platelets’ transcriptome of pan-cancer and 73 commonly known reference genes. These candidate reference genes were verified by another platelets expression data set (GSE89843). Then, we used RT-qPCR to confirm the expression levels of these seven genes in pan-cancer patients and healthy individuals. These RT-qPCR results were analyzed using the internal stability analysis software programs (the comparative Delta CT method, geNorm, NormFinder, and BestKeeper) to rank the candidate genes in the order of decreasing stability. By contrast, the GAPDH gene was stably and constitutively expressed at high levels in all the tested samples. Therefore, GAPDH was recommended as the most suitable reference gene for platelet transcript analysis. In conclusion, our result may play an essential part in establishing a molecular diagnostic platform based on the platelets to diagnose pan-cancer.

Blood-brain barrier (BBB) is a major barrier to drug delivery into the brain in the treatment of central nervous system (CNS) diseases. Blood-brain barrier penetrating peptides (BBPs), a class of peptides that can cross BBB through various mechanisms without damaging BBB, are effective drug candidates for CNS diseases. However, identification of BBPs by experimental methods is time-consuming and laborious. To discover more BBPs as drugs for CNS disease, it is urgent to develop computational methods that can quickly and accurately identify BBPs and non-BBPs. In the present study, we created a training dataset that consists of 326 BBPs derived from previous databases and published manuscripts and 326 non-BBPs collected from UniProt, to construct a BBP predictor based on sequence information. We also constructed an independent testing dataset with 99 BBPs and 99 non-BBPs. Multiple machine learning methods were compared based on the training dataset via a nested cross-validation. The final BBP predictor was constructed based on the training dataset and the results showed that random forest (RF) method outperformed other classification algorithms on the training and independent testing dataset. Compared with previous BBP prediction tools, the RF-based predictor, named BBPpredict, performs considerably better than state-of-the-art BBP predictors. BBPpredict is expected to contribute to the discovery of novel BBPs, or at least can be a useful complement to the existing methods in this area. BBPpredict is freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl.

Gestation length is a complex polygenic trait that affects pig fetal development. The Qingping (QP) pig, a Chinese native black pig breed, is characterized by short gestation length. However, the genetic architecture of short gestation length is still not clear. The present study aimed to explore the genetic architecture of short gestation length in QP pigs. In this study, selective sweep analyses were performed to detect selective sweep signatures for short gestation length traits between 100 QP pigs and 219 pigs from 15 other breeds. In addition, differentially expressed genes for the short gestation length between QP pigs and Large White pigs were detected by RNA sequencing. Comparing candidate genes from these methods with known genes for preterm birth in the database, we obtained 111 candidate genes that were known preterm birth genes. Prioritizing other candidate genes, 839 novel prioritized candidate genes were found to have significant functional similarity to preterm birth genes. In particular, we highlighted EGFR, which was the most prioritized novel candidate relative to preterm birth genes. Experimental validations in placental and porcine trophectoderm cells suggest that EGFR is highly expressed in the QP pigs with short gestation length and could regulate the NF-κΒ pathway and downstream expression of PTGS2. These findings comprehensively identified candidate genes for short gestation length trait at the genomic and transcriptomic levels. These candidate genes provide an important new resource for further investigation and genetic improvement of gestation length.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with poor prognosis. Fatty acid metabolism is associated with cancer progression and a poor prognosis. We searched for long noncoding RNAs (lncRNAs) associated with fatty acid metabolism to predict the overall survival (OS) of patients with LUAD. We obtained lncRNA expression profiles and clinical follow-up data related to fatty acid metabolism in patients with LUAD from The Cancer Genome Atlas and Molecular Signatures database. Patients were randomly divided into training, experimental, and combination groups. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression models were used to construct fatty acid metabolism-related prognostic markers, Kaplan-Meier analysis was used to compare the prognosis of each group, and receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the prognostic model. We used the pRRophetic algorithm to assess the treatment response based on the half-maximal inhibitory concentration (IC50) of each sample in the Genomics of Cancer Drug Sensitivity (GDSC) database. A fatty acid metabolism-related prognostic marker containing seven lncRNAs was constructed to predict OS in LUAD. In the training, test and combination groups, the patients were divided into high- and low-risk groups according to a formula. K–M analysis showed that patients in the high-risk group had poorer prognosis, with significant differences in the subgroup analysis. ROC analysis showed that the predictive ability of the model was more accurate. A clinical prediction nomogram combining lncRNA and clinical features was constructed to accurately predict OS and had high clinical application value. Therapeutics were screened based on the IC50 values of each sample in the GDSC database. We found that A.443654, AUY922, AZ628, A.770041, AZD.0530, AMG.706, and AG.014699 were more effective in high-risk patients. We constructed a 7-lncRNA prognostic model to predict the OS of patients with LUAD. In addition, the predictive nomogram model based on our established seven fatty acid metabolism-related lncRNA signatures provides better clinical value than that of the traditional TNM staging system in predicting the prognosis of patients with LUAD and presents new insights for personalized treatment.

Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.

Aucuba (Garryaceae), which includes approximately ten evergreen woody species, is a genus endemic to East Asia. Their striking morphological features give Aucuba species remarkable ornamental value. Owing to high levels of morphological divergence and plasticity, species definitions of Aucuba remain perplexing and problematic. Here, we sequenced and characterized the complete plastid genomes (plastomes) of three Aucuba species: Aucuba chlorascens, Aucuba eriobotryifolia, and Aucuba japonica. Incorporating Aucuba plastomes available in GenBank, a total of seven Aucuba plastomes, representing six out of ten species of Aucuba, were used for comparative plastome analysis, phylogenetic analysis and divergence time estimation in this study. Comparative analyses revealed that plastomes of Aucuba are highly conserved in size, structure, gene content, and organization, and exhibit high levels of sequence similarity. Phylogenetic reconstruction based on 68 plastid protein-coding genes strongly supported the monophyly of Garryales, Garryaceae and Aucuba. Aucuba eriobotryifolia was sister to the other Aucuba species examined, consistent with its unique fused anther locule. The divergence time of Aucuba was estimated to be approximately late Miocene. Extant Aucuba species derived from recent divergence events associated with the establishment of monsoonal climates in East Asia and climatic fluctuations.

Background: Tyrosine kinase inhibitors that act against epidermal growth factor receptor (EGFR) show strong efficacy against non-small cell lung cancer (NSCLC) involving mutated EGFRs. However, most such patients eventually develop resistance to EGFR-TKIs. Numerous researches have reported that messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) may be involved in EGFR-TKI resistance, but the comprehensive expression profile and competitive endogenous RNA (ceRNA) regulatory network between mRNAs and ncRNAs in EGFR-TKI resistance of NSCLC are incompletely known. We aimed to define a ceRNA regulatory network linking mRNAs and non-coding RNAs that may mediate this resistance.Methods: Using datasets GSE83666, GSE75309 and GSE103352 from the Gene Expression Omnibus, we identified long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs differentially expressed between NSCLC cells that were sensitive or resistant to EGFR-TKIs. The potential biological functions of the corresponding differentially expressed genes were analyzed based KEGG pathways. We combined interactions among lncRNAs, miRNAs and mRNAs in the RNAInter database with KEGG pathways to generate transcriptional regulatory ceRNA networks associated with NSCLC resistance to EGFR-TKIs. Kaplan-Meier analysis was used to assess the ability of core ceRNA regulatory sub-networks to predict the progression-free interval and overall survival of NSCLC. The expression of two core ceRNA regulatory sub-networks in NSCLC was validated by quantitative real-time PCR.Results: We identified 8,989 lncRNAs, 1,083 miRNAs and 3,191 mRNAs that were differentially expressed between patients who were sensitive or resistant to the inhibitors. These DEGs were linked to 968 biological processes and 31 KEGG pathways. Pearson analysis of correlations among the DEGs of lncRNAs, miRNAs and mRNAs identified 12 core ceRNA regulatory sub-networks associated with resistance to EGFR-TKIs. The two lncRNAs ABTB1 and NPTN with the hsa-miR-150–5p and mRNA SERPINE1 were significantly associated with resistance to EGFR-TKIs and survival in NSCLC. These lncRNAs and the miRNA were found to be down-regulated, and the mRNA up-regulated, in a resistant NSCLC cell line relative to the corresponding sensitive cells.Conclusion: In this study, we provide new insights into the pathogenesis of NSCLC and the emergence of resistance to EGFR-TKIs, based on a lncRNA-miRNA-mRNA network.

Background and Aims: The multiple renal cysts (MRC) occur in some patients with noncirrhotic portal hypertension (NCPH) could be a subset of ciliopathy. However, the potential genetic influencers and/or determinants in NCPH with MRC are largely unknown. The aim of this study was to explore the potential candidate variants/genes associated with those patients.Methods: 8,295 cirrhotic patients with portal hypertension were enrolled in cohort 1 and 267 patients affected with NCPH were included in cohort 2. MRC was defined as at least two cysts in both kidneys within a patient detected by ultrasonography or computed tomography. Whole-genome sequencing (WGS) was performed in nine patients (four from cohort 1 and five from cohort 2). Then we integrated WGS and publicly available single-cell RNA sequencing (scRNA-seq) to prioritize potential candidate genes. Genes co-expressed with known pathogenic genes within same cell types were likely associated NCPH with MRC.Results: The prevalence of MRC in NCPH patients (19.5%, 52/267) was significantly higher than cirrhotic patients (6.2%, 513/8,295). Further, the clinical characteristics of NCPH patients with MRC were distinguishable from cirrhotic patients, including late-onset, more prominent portal hypertension however having preserved liver functions. In the nine whole genome sequenced patients, we identified three patients with early onset harboring compound rare putative pathogenic variants in the known disease gene PKHD1. For the remaining patients, by assessing cilia genes profile in kidney and liver scRNA-seq data, we identified CRB3 was the most co-expressed gene with PKHD1 that highly expressed in ureteric bud cell, kidney stromal cell and hepatoblasts. Moreover, we found a homozygous variant, CRB3 p.P114L, that caused conformational changes in the evolutional conserved domain, which may associate with NCPH with MRC.Conclusion: ScRNA-seq enables unravelling cell heterogeneity with cell specific gene expression across multiple tissues. With the boosting public accessible scRNA-seq data, we believe our proposed analytical strategy would effectively help disease risk gene identification.

The lung is one of the most common sites of distant metastasis in breast cancer (BC). Identifying ideal biomarkers to construct a more accurate prediction model than conventional clinical parameters is crucial. MicroRNAs (miRNAs) data and clinicopathological data were acquired from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. miR-663, miR-210, miR-17, miR-301a, miR-135b, miR-451, miR-30a, and miR-199a-5p were screened to be highly relevant to lung metastasis (LM) of BC patients. The miRNA-based risk score was developed based on the logistic coefficient of the individual miRNA. Univariate and multivariate logistic regression selected tumor node metastasis (TNM) stage, age at diagnosis, and miRNA-risk score as independent predictive parameters, which were used to construct a nomogram. The Cancer Genome Atlas (TCGA) database was used to validate the signature and nomogram. The predictive performance of the nomogram was compared to that of the TNM stage. The area under the receiver operating characteristics curve (AUC) of the nomogram was higher than that of the TNM stage in all three cohorts (training cohort: 0.774 vs. 0.727; internal validation cohort: 0.763 vs. 0.583; external validation cohort: 0.925 vs. 0.840). The calibration plot of the nomogram showed good agreement between predicted and observed outcomes. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA) of the nomogram showed that its performances were better than that of the TNM classification system. Functional enrichment analyses suggested several terms with a specific focus on LM. Subgroup analysis showed that miR-30a, miR-135b, and miR-17 have unique roles in lung metastasis of BC. Pan-cancer analysis indicated the significant importance of eight predictive miRNAs in lung metastasis. This study is the first to establish and validate a comprehensive lung metastasis predictive nomogram based on the METABRIC and TCGA databases, which provides a reliable assessment tool for clinicians and aids in appropriate treatment selection.

Lymphoma is a systemic malignancy, originating from the lymphatic system, which accounts for 3 to 4% of all tumors. There are two major subtypes of lymphoma, namely, diffuse large B-cell lymphoma (DLBCL) and Hodgkin’s lymphoma (HL). Elucidation of the pathogenesis of these two lymphoma types is crucial for the identification of potential therapeutic targets. Compared with the corresponding knowledge of other diseases, the understanding of the regulatory networks involved in DLBCL and HL is relatively deficient. To address this, we comprehensively analyzed the mRNAs, lncRNAs, and miRNAs that were differentially expressed between normal and tumor samples of DLBCL and HL. In addition, functional enrichment analysis of the differentially expressed mRNAs was performed. We constructed two specific ceRNA networks of DLBCL and HL. The pathways enriched by dysregulated mRNAs in DLBCL and HL were mainly involved in immune responses, transcription process, and metabolism process. The ceRNA network analysis revealed that 45 ceRNAs were shared between the two ceRNA networks, including five pivotal lncRNAs (MALAT1, CTBP1-AS, THUMPD3-AS, PSMA3-AS1, and NUTM2A-AS1). In addition, we proposed a DLBCL survival risk model based on a DLBCL-specific network constructed by Lasso regression analysis. The model, which is based on eight mRNAs, exhibited excellent performance in regard to predicting outcomes in DLBCL patients, with a p value of 0.0017 and AUC of 0.9783. In summary, although the molecular mechanisms underlying tumorigenesis in DLBCL and HL were quite different, the same pivotal lncRNAs acted as key regulators. Our findings identify novel potential prognostic and therapeutic targets for DLBCL and HL.

HFE-related Hemochromatosis is the most common genetic iron overload disease in European populations, particularly of Nordic or Celtic ancestry. It is reported that the HFE p.C282Y mutation is present in 1/10 people of northern European descent, resulting in one in two hundred people will be homozygous. However, the HFE p.C282Y heterozygosity is virtually absent among East Asians, including Japanese, Koreans, and Chinese. In this article, we report a case of HFE-related hemochromatosis caused by compound heterozygosity HFE p.C282Y/p.R71X. This is the first report of hemochromatosis associated with HFE p.C282Y mutation in China.

Rotor syndrome, a rare autosomal-recessive genetic disorder characterized by conjugated hyperbilirubinemia, is caused by biallelic pathogenic variants in both SLCO1B1 and SLCO1B3 genes. Long interspersed nuclear elements (LINEs) make up about 17% of the human genome and insertion of LINE-1 in genes can result in genetic diseases. In the current study, we examined SLCO1B1 and SLCO1B3 genes in two Chinese patients diagnosed with Rotor syndrome based on laboratory tests. In one patient, a novel exon 4 inversion variant was identified. This variant may have been induced by LINE-1 retrotransposon insertion into SLCO1B3 intron 3, and was identified using genome walking. Splicing assay results indicated that the exon inversion, resulting in SLCO1B3 exon 4 (122 bp) exclusion in the mature mRNA, might generate a premature termination codon. Here, we describe an exon inversion contributing to the molecular etiology of Rotor syndrome. Our results may inform future diagnoses and guide drug prescriptions and genetic counseling.

Age-related phenotypes are characterized by genetic heterogeneity attributed to an uncertain role of evolution in establishing their molecular mechanisms. Here, we performed univariate and pleiotropic meta-analyses of 24 age-related phenotypes dealing with such evolutionary uncertainty and leveraging longitudinal information. Our analysis identified 237 novel single nucleotide polymorphisms (SNPs) in 199 loci with phenotype-specific (61 SNPs) and pleiotropic (176 SNPs) associations and replicated associations for 160 SNPs in 68 loci in a modest sample of 26,371 individuals from five longitudinal studies. Most pleiotropic associations (65.3%, 115 of 176 SNPs) were impacted by heterogeneity, with the natural-selection—free genetic heterogeneity as its inevitable component. This pleiotropic heterogeneity was dominated (93%, 107 of 115 SNPs) by antagonistic genetic heterogeneity, a phenomenon that is characterized by antagonistic directions of genetic effects for directly correlated phenotypes. Genetic association studies of age-related phenotypes addressing the evolutionary uncertainty in establishing their molecular mechanisms have power to substantially improve the efficiency of the analyses. A dominant form of heterogeneous pleiotropy, antagonistic genetic heterogeneity, provides unprecedented insight into the genetic origin of age-related phenotypes and side effects in medical care that is counter-intuitive in medical genetics but naturally expected when molecular mechanisms of age-related phenotypes are not due to direct evolutionary selection.

Cardiac hypertrophy is an adaptive cardiac response that accommodates the variable hemodynamic demands of the human body during extended periods of preload or afterload increase. In recent years, an increasing number of studies have pointed to a potential connection between myocardial hypertrophy and abnormal expression of non-coding RNAs. Circular RNA (circRNA), as one of the non-coding RNAs, plays an essential role in cardiac hypertrophy. However, few studies have systematically analyzed circRNA-related competing endogenous RNA (ceRNA) regulatory networks associated with cardiac hypertrophy. Therefore, we used public databases from online prediction websites to predict and screen differentially expressed mRNAs and miRNAs and ultimately obtained circRNAs related to cardiac hypertrophy. Based on this result, we went on to establish a circRNAs-related ceRNA regulatory network. This study is the first to establish a circRNA-mediated ceRNA regulatory network associated with myocardial hypertrophy. To verify the results of our analysis, we used PCR to verify the differentially expressed mRNAs and miRNAs in animal myocardial hypertrophy model samples. Our findings suggest that three mRNAs (Col12a1, Thbs1, and Tgfbr3), four miRNAs (miR-20a-5p, miR-27b-3p, miR-342-3p, and miR-378a-3p), and four related circRNAs (circ_0002702, circ_0110609, circ_0013751, and circ_0047959) may play a key role in cardiac hypertrophy.

Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.