Synergy of gut microbiota and host genome in driving heterosis expression of chickens

Heterosis has been widely utilized in agricultural production.Despite over a century of extensive research,the underlying mechanisms of heterosis remain elusive.Most hypotheses and research have focused on the genetic basis of heterosis.However,the potential role of gut microbiota in heterosis has been largely ignored.Here,we carefully design a crossbreeding experiment with two distinct broiler breeds and conduct 16S rRNA amplicon and transcriptome sequencing to investigate the synergistic role of gut microbiota and host genes in driving heterosis.We find that the breast muscle weight of hybrids exhibits a high heterosis,6.28%higher than the mid-parent value.A notable difference is observed in the composition and potential function of cecal microbiota between hybrids and their parents.Over 90%of differentially colonized microbiota and differentially expressed genes exhibit nonadditive patterns.Integrative analyses uncover associations between nonadditive genes and nonadditive microbiota,including a connection between the expression of cellular signaling pathways and metabolism-related genes and the abundance of Odor-ibacter,Oscillibacter,and Alistipes in hybrids.Moreover,higher abundances of these microbiota are related to better meat yield.In summary,these findings highlight the importance of gut microbiota in heterosis,serving as crucial factors that modulateheterosis expression in chickens.

Effects of antioxidant‑rich Lactiplantibacillus plantarum inoculated alfalfa silage on rumen fermentation, antioxidant and immunity status, and mammary gland gene expression in dairy goats

Background Milk synthesis in lactating animals demands high energy metabolism,which results in an increased production of reactive oxygen metabolites(ROM)causing an imbalance between oxidants and antioxidants thereby inducing oxidative stress(OS)on the animals.To mitigate OS and postpartum disorders in dairy goats and gain insight into the impact of dietary choices on redox status during lactation,a feeding trial was conducted using alfalfa silage inoculated with a high-antioxidant strain of Lactiplantibacillus plantarum.Methods Twenty-four Guanzhong dairy goats(38.1±1.20 kg)were randomly assigned to two dietary treatments:one containing silage inoculated with L.plantarum MTD/1(RSMTD-1),and the other containing silage inoculated with high antioxidant activity L.plantarum 24-7(ES24-7).Results ES24-7-inoculated silage exhibited better fermentation quality and antioxidant activity compared to RSMTD-1.The ES24-7 diet elevated the total antioxidant capacity(T-AOC),superoxide dismutase(SOD),glutathione peroxi-dase(GSH-Px),and catalase(CAT)activities in milk,serum,and feces of lactating goats(with the exception of T-AOC in milk).Additionally,the diet containing ES24-7 inoculated silage enhanced casein yield,milk free fatty acid(FFA)content,and vitamin A level in the goats’milk.Furthermore,an increase of immunoglobulin(Ig)A,IgG,IgM,inter-leukin(IL)-4,and IL-10 concentrations were observed,coupled with a reduction in IL-1β,IL-2,IL-6,interferon(IFN)-γ,and tumor necrosis factor(TNF)-αconcentrations in the serum of lactating goats fed ES24-7.Higher concentrations of total volatile fatty acid(VFA),acetate,and propionate were observed in the rumen fluid of dairy goats fed ES24-7 inoculated silage.Moreover,the diet containing ES24-7 inoculated silage significantly upregulated the expression of nuclear factor erythroid 2 like 2(NFE2L2),beta-carotene oxygenase 1(BCO1),SOD1,SOD2,SOD3,GPX2,CAT,glu-tathione-disulfide reductase(GSR),and heme oxygenase 1(HMOX1)genes in the mammary gland,while decreased the levels of NADPH oxidase 4(NOX4),TNF,and interferon gamma(IFNG).Conclusions These findings indicated that feeding L.plantarum 24-7 inoculated alfalfa silage not only improved rumen fermentation and milk quality in lactating dairy goats but also boosted their immunity and antioxidant status by modulating the expression of several genes related to antioxidant and inflammation in the mammary gland.

Effects of pyraclostrobin on growth,oxidative stress,and gene expression in relation to stress and ATP-binding cassette transporters in Tetrahymena thermophila

Pyraclostrobin(PYR),a widely used fungicide,has negative effects on fish and algae,but its toxicity in protozoa remains unclear.In this study,the effects of PYR on the growth,oxidative stress,and gene expression related to stress and ATP-binding cassette(ABC)transporters in Tetrahymena thermophila were investigated.The result showed that the 96-h IC_(50)of PYR against T.thermophila was 17.2 mg/L.Moreover,PYR inhibited the growth of T.thermophila in concentration-or time-dependent manner.A morphological study revealed that the shape and size of T.thermophila changed,and damage of cell membrane surface was observed by scanning electron microscopy after 96 h of PYR exposure.The activities of superoxide dismutase(SOD)and catalase(CAT)increased throughout the experiment.In contrast,the glutathione(GSH)content was increased at 24 h and 48 h of exposure and decreased at 96 h.Moreover,a significant increase in malondialdehyde(MDA)level was observed in T.thermophila after96 h of exposure.Furthermore,PYR upregulated the HSP703,HSP705,GPx2,and ABAC15 gene expression in the 0.1–5-mg/L groups and downregulated the HSP704,HSP90,TGR,and ABCC52 mRNA levels at 96 h of exposure.These results suggest that PYR may exert adverse effects on T.thermophila by inducing oxidative stress and changing the gene expression related to ABC transporters and stress,which may enrich the understanding of the toxicity mechanism of PYR in aquatic organisms and provide reference data for aquatic ecological risk assessments.

Genome wide investigation of Hsf gene family in Phoebe bournei:identification,evolution,and expression after abiotic stresses

Heat shock transcription factors(Hsfs)have important roles during plant growth and development and responses to abiotic stresses.The identification and func-tion of Hsf genes have been thoroughly studied in various herbaceous plant species,but not woody species,especially Phoebe bournei,an endangered,unique species in China.In this study,17 members of the Hsf gene family were identi-fied from P.bournei using bioinformatic methods.Phyloge-netic analysis indicated that PbHsf genes were grouped into three subfamilies:A,B,and C.Conserved motifs,three-dimensional structure,and physicochemical properties of the PbHsf proteins were also analyzed.The structure of the PbHsf genes varied in the number of exons and introns.Pre-diction of cis-acting elements in the promoter region indi-cated that PbHsf genes are likely involved in responses to plant hormones and stresses.A collinearity analysis dem-onstrated that expansions of the PbHsf gene family mainly take place via segmental duplication.The expression levels of PbHsf genes varied across different plant tissues.On the basis of the expression profiles of five representative PbHsf genes during heat,cold,salt,and drought stress,PbHsf pro-teins seem to have multiple functions depending on the type of abiotic stress.This systematic,genome-wide investigation of PbHsf genes in P.bournei and their expression patterns provides valuable insights and information for further func-tional dissection of Hsf proteins in this endangered,unique species.

Molecular mechanism of hypoxia and alpha-ketoglutaric acid on collagen expression in scleral fibroblasts

AIM:To investigate the molecular mechanisms underlying the influence of hypoxia and alpha-ketoglutaric acid(α-KG)on scleral collagen expression.METHODS:Meta-analysis and clinical statistics were used to prove the changes in choroidal thickness(ChT)during myopia.The establishment of a hypoxic myopia model(HYP)for rabbit scleral fibroblasts through hypoxic culture and the effects of hypoxia andα-KG on collagen expression were demonstrated by Sirius red staining.Transcriptome analysis was used to verify the genes and pathways that hypoxia andα-KG affect collagen expression.Finally,real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR)was used for reverse verification.RESULTS:Meta-analysis results aligned with clinical statistics,revealing a thinning of ChT,leading to scleral hypoxia.Sirius red staining indicated lower collagen expression in the HYP group and higher collagen expression in the HYP+α-KG group,showed that hypoxia reduced collagen expression in scleral fibroblasts,whileα-KG can elevated collagen expression under HYP conditions.Transcriptome analysis unveiled the related genes and signaling pathways of hypoxia andα-KG affect scleral collagen expression and the results were verified by RT-qPCR.CONCLUSION:The potential molecular mechanisms through which hypoxia andα-KG influencing myopia is unraveled and three novel genes TLCD4,TBC1D4,and EPHX3 are identified.These findings provide a new perspective on the prevention and treatment of myopia via regulating collagen expression.

A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data.However,labeling large datasets demands significant human,time,and financial resources.Although active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition datasets.This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics.This paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale variations.The method is divided into two primary phases.First,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction capabilities.Second,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition accuracy.In the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled dataset.These features are then weighted through a self-attention mechanism with rank regularization.Subsequently,data from the low-weighted set is relabeled to further refine the model’s feature extraction ability.The pre-trained model is then utilized in active learning to select and label information-rich samples more efficiently.Experimental results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

Genome-wide identification and expression profiling of photosystem II(PsbX)gene family in upland cotton(Gossypium hirsutum L.)

Background Photosystem II(PSII)constitutes an intricate assembly of protein pigments,featuring extrinsic and intrinsic polypeptides within the photosynthetic membrane.The low-molecular-weight transmembrane protein PsbX has been identified in PSII,which is associated with the oxygen-evolving complex.The expression of PsbX gene protein is regulated by light.PsbX’s central role involves the regulation of PSII,facilitating the binding of quinone molecules to the Qb(PsbA)site,and it additionally plays a crucial role in optimizing the efficiency of photosynthesis.Despite these insights,a comprehensive understanding of the PsbX gene’s functions has remained elusive.Results In this study,we identified ten PsbX genes in Gossypium hirsutum L.The phylogenetic analysis results showed that 40 genes from nine species were classified into one clade.The resulting sequence logos exhibited substantial conservation across the N and C terminals at multiple sites among all Gossypium species.Furthermore,the ortholo-gous/paralogous,Ka/Ks ratio revealed that cotton PsbX genes subjected to positive as well as purifying selection pressure might lead to limited divergence,which resulted in the whole genome and segmental duplication.The expression patterns of GhPsbX genes exhibited variations across specific tissues,as indicated by the analysis.Moreover,the expression of GhPsbX genes could potentially be regulated in response to salt,intense light,and drought stresses.Therefore,GhPsbX genes may play a significant role in the modulation of photosynthesis under adverse abiotic conditions.Conclusion We examined the structure and function of PsbX gene family very first by using comparative genom-ics and systems biology approaches in cotton.It seems that PsbX gene family plays a vital role during the growth and development of cotton under stress conditions.Collectively,the results of this study provide basic information to unveil the molecular and physiological function of PsbX genes of cotton plants.

Global-local combined features to detect pain intensity from facial expression images with attention mechanism

The estimation of pain intensity is critical for medical diagnosis and treatment of patients.With the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial expression and behavioral analysis shows a potential value in clinical applications.This paper reports a framework of convolutional neural network with global and local attention mechanism(GLA-CNN)for the effective detection of pain intensity at four-level thresholds using facial expression images.GLA-CNN includes two modules,namely global attention network(GANet)and local attention network(LANet).LANet is responsible for extracting representative local patch features of faces,while GANet extracts whole facial features to compensate for the ignored correlative features between patches.In the end,the global correlational and local subtle features are fused for the final estimation of pain intensity.Experiments under the UNBC-McMaster Shoulder Pain database demonstrate that GLA-CNN outperforms other state-of-the-art methods.Additionally,a visualization analysis is conducted to present the feature map of GLA-CNN,intuitively showing that it can extract not only local pain features but also global correlative facial ones.Our study demonstrates that pain assessment based on facial expression is a non-invasive and feasible method,and can be employed as an auxiliary pain assessment tool in clinical practice.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

Genome-Wide Identification of Tomato (Solanum lycopersicum L.) CKX Gene Family and Expression Analysis in the Callus Tissue under Zeatin Treatment

The cytokinin oxidase/dehydrogenase(CKX)enzyme is essential for controlling thefluctuating levels of endogen-ous cytokinin(CK)and has a significant impact on different aspects of plant growth and development.Nonethe-less,there is limited knowledge about CKX genes in tomato(Solanum lycopersicum L.).Here we performed genome-wide identification and analysis of nine SlCKX family members in tomatoes using bioinformatics tools.The results revealed that nine SlCKX genes were unevenly distributed onfive chromosomes(Chr.1,Chr.4,Chr.8,Chr.10,and Chr.12).The amino acid length,isoelectric points,and molecular weight of the nine SlCKX proteins ranged from 453 to 553,5.77 to 8.59,and 51.661 to 62.494 kD,respectively.Subcellular localization analysis indi-cated that SlCKX2 proteins were located in both the vacuole and cytoplasmic matrix;SlCKX3 and SlCKX5 pro-teins were located in the vacuole;and SlCKX1,4,6,7,8,and 9 proteins were located in the cytoplasmic matrix.Furthermore,we observed differences in the gene structures and phylogenetic relationships of SlCKX proteins among different members.SlCKX1-9 were positioned on two out of the three branches of the CKX phylogenetic tree in the multispecies phylogenetic tree construction,revealing their strong conservation within phylogenetic subgroups.Unique patterns of expression of CKX genes were noticed in callus cultures exposed to varying con-centrations of exogenous ZT,suggesting their roles in specific developmental and physiological functions in the regeneration system.These results may facilitate subsequent functional analysis of SlCKX genes and provide valu-able insights for establishing an efficient regeneration system for tomatoes.

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

Expression of cyclin-dependent kinase 9 is positively correlated with the autophagy level in colon cancer

BACKGROUND Cyclin-dependent kinase 9(CDK9)expression and autophagy in colorectal cancer(CRC)tissues has not been widely studied.CDK9,a key regulator of transcription,may influence the occurrence and progression of CRC.The expression of auto-phagy-related genes BECN1 and drug resistance factor ABCG2 may also play a role in CRC.Under normal physiological conditions,autophagy can inhibit tumorigenesis,but once a tumor forms,autophagy may promote tumor growth.Therefore,understanding the relationship between autophagy and cancer,partic-ularly how autophagy promotes tumor growth after its formation,is a key motivation for this research.AIM To investigate the relationship between CDK9 expression and autophagy in CRC,assess differences in autophagy between left and right colon cancer,and analyze the associations of autophagy-related genes with clinical features and prognosis.METHODS We collected tumor tissues and paracarcinoma tissues from colon cancer patients with liver metastasis to observe the level of autophagy in tissues with high levels of CDK9 and low levels of CDK9.We also collected primary tissue from left and right colon cancer patients with liver metastasis to compare the autophagy levels and the expression of BECN1 and ABCG2 in the tumor and paracarcinoma tissues.RESULTS The incidence of autophagy and the expression of BECN1 and ABCG2 were different in left and right colon cancer,and autophagy might be involved in the occurrence of chemotherapy resistance.Further analysis of the rela-tionship between the expression of autophagy-related genes CDK9,ABCG2,and BECN1 and the clinical features and prognosis of colorectal cancer showed that the high expression of CDK9 indicated a poor prognosis in colorectal cancer.CONCLUSION This study laid the foundation for further research on the combination of CDK9 inhibitors and autophagy inhibitors in the treatment of patients with CRC.

Genome-Wide Discovery and Expression Profiling of the SWEET Sugar Transporter Gene Family in Woodland Strawberry (Fragaria vesca) under Developmental and Stress Conditions: Structural and Evolutionary Analysis

The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.

High patatin like phospholipase domain containing 8 expression as a biomarker for poor prognosis of colorectal cancer

BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

Tissue inhibitor of metalloproteinase-3 expression affects clinicopathological features and prognosis of aflatoxin B1-related hepatocellular carcinoma

BACKGROUND The dysregulation of tissue inhibitor of metalloproteinase-3(TIMP3)was positively correlated with the progression of hepatocellular carcinoma(HCC).However,it is not clear whether TIMP3 expression is associated with the clinico-pathological features and prognosis of aflatoxin B1(AFB1)-related HCC(AHCC).A retrospective study,including 182 patients with AHCC,was conducted to explore the link between TIMP3 expression in cancerous tissues and the clinico-pathological characteristics and prognosis of AHCC.TIMP3 expression was detected by immunohistochemistry and its effects on the clinicopathological features and prognosis of AHCC were evaluated by Kaplan-Meier survival analysis and Cox regression survival analysis.Odds ratio,hazard ratio(HR),median overall survival time(MST),median tumor recurrence-free survival time(MRT),and corresponding 95%confidential interval(CI)was calculated to RESULTS Kaplan-Meier survival analysis showed that compared with high TIMP3 expression,low TIMP3 expression in tumor tissues significantly decreased the MST(36.00 mo vs 18.00 mo)and MRT(32.00 mo vs 16 mo)of patients with AHCC.Multivariate Cox regression survival analysis further proved that decreased expression of TIMP3 increased the risk of death(HR=2.85,95%CI:2.04-4.00)and tumor recurrence(HR=2.26,95%CI:1.57-3.26).Furthermore,decreased expression of TIMP3 protein in tissues with AHCC was significantly correlated with tumor clinicopatho-logical features,such as tumor size,tumor grade and stage,tumor microvessel density,and tumor blood invasion.Additionally,TIMP3 protein expression was also negatively associated with amount of AFB1-DNA adducts in tumor tissues.CONCLUSION These findings indicate that the dysregulation of TIMP3 expression is related to AHCC biological behaviors and affects tumor outcome,suggesting that TIMP3 may act as a prognostic biomarker for AHCC.

Effect of Chuanzhifang component (ZGC) on macrophage inflammatory injury based on whole gene expression profile

Objective: The effect of Chuanzhi Fang (ZGC) on the whole genome expression profile of RAW264.7 cells activated by lipopolysaccharide (LPS) was analyzed, and to explore the possible mechanism of action and core target of this formula on macrophage inflammatory injury at the overall level. Methods: A model of LPS-induced inflammation in RAW264.7 cells was constructed, and the effect of ZGC intervention on the genome-wide expression of inflammatory macrophages 3was examined by gene microarray technology, GO/KEGG enrichment analysis was performed for significantly differentially expressed genes among each group. Results: The results of genome-wide expression profiling microarray analysis showed that the ZGC intervention group upregulated the expression of 5 genes including C4bp and inhibited the expression of 22 genes including Mgat3, Psma6, and Siglecg relative to the LPS model group. KEGG signaling pathway analysis results showed that ZGC mainly acted through cytokine receptor interaction and the C-type lectin receptor signaling pathway. Conclusion: ZGC can interfere with the abnormal expression of 27 genes in inflammatory macrophages, and the related genes may exert corresponding anti-inflammatory effects by affecting cytokine receptor interactions, C-type lectin receptor signaling pathway, and TLR4/ NF-κB signaling pathway.

Decoding Retinoblastoma: Differential Gene Expression

Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.

Study on the expression and prognostic relationship of MYL6B in liver cancer based on bioinformatics

BACKGROUND Primary liver cancer is a prevalent and deadly cancer type.Despite treatment advances,prognosis remains poor,with high recurrence rates.Early detection is crucial but challenging due to the disease’s insidious nature.Myosin proteins play significant roles in cancer development,influencing cell migration,invasion,and tumor suppression.MYL6B,a myosin light chain,is involved in various cellular processes and has been associated with poor prognosis in colorectal adenocarcinoma and potential as a biomarker in breast cancer.AIM To investigate the expression of MYL6B in liver hepatocellular carcinoma(LIHC)and its impact on prognosis and potential mechanisms of action using bioinformatics methods.METHODS The expression of MYL6B in pan-cancer and normal tissues was analyzed using the gene expression profiling interactive analysis 2 and tumor immune estimation resource databases.The expression level of MYL6B in LIHC tissues and its relationship with prognosis were analyzed,immunohistochemical analysis of MYL6B and its effect on immune cell infiltration,and the protein network were further studied.RESULTS MYL6B was highly expressed in diffuse large b-cell lymphoma,LIHC,pancreatic adenocarcinoma,skin cutaneous melanoma,thymoma,uterine corpus endometrial carcinoma,uterine carcinosarcoma,and lowly expressed in kidney chromophobe,acute myeloid leukemia,testicular germ cell tumors.The expression level of MYL6B was significantly different between cancer and normal tissues.It had a significant impact on both overall survival and disease-free survival.MYL6B is highly expressed in hepatocellular carcinoma and its expression level increases with cancer progression.High MYL6B expression is associated with poor prognosis in terms of overall survival and recurrence-free survival.The immunohistochemical level of MYL6B is high in hepatocellular carcinoma tissues,and MYL6B has a high level of immune infiltration inflammation.In protein network analysis,MYL6B is correlated with MYL2,MYL6,MYL9,MYLK4,MYLK2,MYL12A,MYL12B,MYH11,MYH9 and MYH10.CONCLUSION The expression level of MYL6B in LIHC was significantly higher than in normal liver tissues,and it was correlated with the degree of differentiation survival rate,and immune infiltration.MYL6B is a potential target for LIHC treatment.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.