Comparative and Functional Genomics最新文献

Carotid body tumor (CBT) resection is a complex surgical procedure often resulting in extended postoperative length of stay (PLOS) due to potential nerve injuries, arterial damage, and wound complications. The neutrophil-to-lymphocyte ratio (NLR) is a known marker of systemic inflammation and has been associated with adverse outcomes in various surgical settings. However, the relationship between preoperative NLR and PLOS in CBT patients has not been explored. This study aims to investigate the association between preoperative NLR and PLOS in CBT resections, particularly examining whether elevated NLR correlates with longer hospital stays and potentially hinders recovery. In this retrospective cohort study, we analyzed data from 231 CBT patients who underwent resection at Changhai Hospital, Shanghai, between 2008 and 2020. Patients were grouped based on their PLOS (short, medium, and long stays), and NLR was calculated from peripheral blood samples taken preoperatively. Univariate and multivariate regression models adjusted for sociodemographic and operative factors, including Shamblin classification, were used to examine the relationship between NLR and PLOS. Elevated preoperative NLR has been found to be significantly correlated with prolonged PLOS, with each incremental increase in NLR corresponding to an approximate extension of 0.12 days in PLOS after adjusting for confounding factors. Stratified analysis revealed that this association was most pronounced in patients with Shamblin II tumors, likely due to the moderate tumor size and adhesion in these cases, which necessitates more extensive dissection and increases vulnerability to nerve injury. Elevated preoperative NLR may serve as a predictor of prolonged recovery in CBT resections, particularly for Shamblin II cases. This finding highlights the potential utility of NLR in preoperative assessment and patient management to optimize surgical timing and reduce hospital stays. Further research with larger cohorts is needed to confirm the predictive value of NLR and explore its clinical application in surgical planning for CBT patients.

Rotator cuff injuries are a common cause of shoulder pain and dysfunction, with chronic inflammation complicating recovery. Recent advances in single-cell RNA sequencing (scRNA-seq) have provided new insights into the immune cell interactions within the rotator cuff microenvironment during injury and healing. This review focuses on the application of scRNA-seq to explore the roles of immune and nonimmune cells, including macrophages, T-cells, fibroblasts, and myofibroblasts, in driving inflammation, tissue repair, and fibrosis. We discuss how immune cell crosstalk and interactions with the extracellular matrix influence the progression of healing or pathology. Single-cell analyses have identified distinct molecular signatures associated with chronic inflammation, which may contribute to persistent tissue damage. Additionally, we highlight the therapeutic potential of targeting inflammation in rotator cuff repair, emphasizing personalized medicine approaches. Overall, the integration of scRNA-seq in studying rotator cuff injuries enhances our understanding of the cellular mechanisms involved and offers new perspectives for developing targeted treatments in regenerative medicine.

Aegle marmelos, known for its spiky appearance, is a versatile tree found worldwide. In the Indian medical tradition, this therapeutic tree is utilized to treat various ailments. It is commonly propagated through seeds, although they have a limited lifespan and are susceptible to insect damage. Due to the variability of seed offspring, standardized varieties are not readily available. Molecular identification was performed for the plant species to be as a fingerprint identification based on genomic basic. Hence, this study manipulated the in vitro multiplication for enhancing Aegle marmelos traits through variation in media type and composition. In phase one of the experiment, successful micropropagation has been easily achieved with shoot tip culture on two growth in vitro media: Murashige and Skoog (MS) medium and woody plant medium (WPM) with different concentrations (one-fourth, one-half, three-fourths, and full power media) with two sucrose concentration 20 and 30 g/L. The growth parameters measured indicated a heightened response to both MS and WPM media, each with its distinct composition. The genetic variation via intersimple sequence repeat (ISSR) molecular marker in the first phase was 35.5%. In phase two, the hormonal treatment was applied for the best media choice from Phase 1. During the second phase of multiplication and rooting stages with phytohormones, the optimal treatments were chosen to maximize yields. In the multiplication stage, the most favorable conditions, as determined by morphological parameters, were achieved with full MS medium supplemented with 30 g sucrose, 0.1 mg/L Kin, and 0.75 mg/L BAP. In contrast, for the rooting stage, the optimal treatment consisted of one-fourth MS medium supplemented with 15 g sucrose, 0.5 mg/L Kin, 0.1 g/L activated charcoal, and 15 mg/L IBA. Physiological parameters exhibited variability, with each metabolite displaying distinct optimal conditions. Catalase plays a crucial role in decomposing hydrogen peroxide to protect cells, tissues, and organs. This research effectively enhanced the in vitro micropropagation of Aegle marmelos by determining the most efficacious medium formulations and hormonal treatments for shoot multiplication and roots, while also illustrating the influence of WPM on catalase enzyme activity enhancement.

In environments with high levels of stress conditions, plants accumulate various metabolic products under stress conditions. Among these products, amino acids have a cardinal role in supporting and maintaining plant developmental processes. The increase in proline content and stress tolerance in plants has been found optimistic, suggesting the importance of proline in mitigating stress through osmotic adjustments. Exogenous application and pretreatment of plants with proline increase growth and development under various stressful conditions, but excessive proline has negative influence on growth. Proline has two biosynthetic routes: glutamate or the ornithine pathway, and whether plants synthesize proline by glutamate or ornithine precursors is still debatable as relatively little is known about it. Plants have the innate machinery to synthesize proline from both pathways, but the switch of a particular pathway under which it can be activated and deactivated depends upon various factors. Therefore, in this review, we elucidate the importance of proline in stress mitigation; the optimal amount of proline required for maximum benefit; levels at which it inhibits the growth, conditions, and factors that regulate proline biosynthesis; and lastly, how we can benefit from all these answers to obtain better stress tolerance in plants.

Background: The prognosis for lung adenocarcinoma (LUAD) is poor, and the recurrence rate is high. Thus, to evaluate patients’ prognoses and direct therapy choices, new prognostic markers are desperately needed.

Methods: First, gene modules associated with LUAD were identified by weighted gene coexpression network analysis (WGCNA) analysis. The expression profiles obtained were intersected with the differential expressed genes taken between LUAD samples and paracancerous samples. Afterward, stepwise regression analysis and the LASSO were used to compress the genes further, and a risk model was created. Furthermore, a nomogram based on risk scores and clinical features was created to validate the model. After that, the distinctions between the pertinent biological processes and signaling pathways among the various subgroups were investigated. Additionally, drug sensitivity testing, immunotherapy, immune infiltration analysis, and enrichment analysis were carried out. Finally, the biological role of ANLN in LUAD was explored by qPCR, cell scratch assay, and transwell.

Results: A total of 257 intersected genes were obtained by taking the intersection of the differential genes between 2866 LUAD samples and paraneoplastic samples with the module genes after we screened two particular modules that had the strongest link with LUAD by WGCNA. ANLN, CASS4, and NMUR1 were found to be distinctive genes for the development of risk models after the intersecting genes were screened to find 176 genes linked to the prognosis for LUAD. Based on risk assessments, high- and low-risk groups of LUAD patients were divided. Low-risk patients exhibited a significantly higher overall survival (OS) than those in the high-risk group. Expression of model genes correlates with infiltration of the vast majority of immune cells. Significant differences in the biological pathways, immune microenvironment, and abundance of immune cell infiltration were found between the two groups. The drug sensitivity study showed that patients in the high-risk group had higher IC₅₀ values for BMS-754807_2171 and Doramapimod_10424. Finally, in vitro experiments demonstrated that knocking down ANLN noticeably inhibited the viability, migration, and invasion of A549 cells.

Conclusion: This study may provide a theoretical reference for future exploration of potential diagnostic and prognostic biomarkers for LUAD.

Prostate cancer (PCa) continues to pose substantial clinical challenges, with molecular heterogeneity significantly impacting therapeutic decision-making and disease trajectories. Emerging evidence implicates protein lactylation—a novel epigenetic regulatory mechanism—in oncogenic processes, though its prognostic relevance in PCa remains underexplored. Through integrative bioinformatics interrogation of lactylation-associated molecular signatures, we established prognostic correlations using multivariable feature selection methodologies. Initial screening via differential expression analysis (limma package) coupled with Cox proportional hazards modeling revealed 11 survival-favorable regulators and 16 hazard-associated elements significantly linked to biochemical recurrence. To enhance predictive precision, ensemble machine learning frameworks were implemented, culminating in a 10-gene lactylation signature demonstrating robust discriminative capacity (concordance index = 0.738) across both primary (TCGA-PRAD) and external validation cohorts (DKFZ). Multivariable regression confirmed the lactylation score’s prognostic independence, exhibiting prominent associations with clinicopathological parameters including tumor staging and metastatic potential. The developed clinical-molecular nomogram achieved superior predictive accuracy (C − index > 0.7) through the synergistic integration of biological and clinical covariates. Tumor microenvironment deconvolution uncovered distinct immunological landscapes, with high-risk stratification correlating with enriched stromal infiltration and immunosuppressive phenotypes. Pathway enrichment analyses implicated chromatin remodeling processes and cytokine-mediated inflammatory cascades as potential mechanistic drivers of prognostic divergence. Therapeutic vulnerability profiling demonstrated differential response patterns: low-risk patients exhibited enhanced immune checkpoint inhibitor responsiveness, whereas high-risk subgroups showed selective chemosensitivity to docetaxel and mitoxantrone. Functional validation in PC-3 models revealed AK5 silencing induced proapoptotic effects, suppressed metastatic potential of migration and invasion, and modulated immune checkpoint regulation through CD276 coexpression. These multimodal findings position lactylation dynamics, particularly AK5-mediated pathways, as promising therapeutic targets and stratification biomarkers in PCa management.

Background: Osteosarcoma (OS) is a highly aggressive bone malignancy prevalent in children and adolescents, characterized by poor prognosis and limited therapeutic options. The tumor microenvironment (TME) and cell death mechanisms such as PANoptosis—comprising pyroptosis, apoptosis, and necroptosis—play critical roles in tumor progression and immune evasion. This study is aimed at exploring the PANoptosis landscape in OS using single-cell RNA sequencing (scRNA-seq) and at developing a robust prognostic model using machine learning algorithms.

Methods: Single-cell sequencing data for OS were obtained from the GEO database (GSE162454), and bulk transcriptome data were sourced from the TARGET and GEO databases. Data integration, dimensionality reduction, and cell clustering were performed using UMAP and t-SNE. PANoptosis-related genes were identified, and their expression profiles were used to score and categorize cells into PANoptosis-high and PANoptosis-low groups. A comprehensive prognostic model was constructed using 101 machine learning algorithms, including CoxBoost, to predict patient outcomes. The model’s performance was validated across multiple cohorts, and its association with the mutation landscape and TME was evaluated.

Results: The scRNA-seq analysis revealed 14 distinct cell clusters within OS, with significant PANoptosis activation observed in cancer-associated fibroblasts (CAFs), myeloid cells, osteoblasts, and osteoclasts. Differentially expressed genes between PANoptosis-high and PANoptosis-low groups were identified, and cell communication analysis showed enhanced interaction patterns in the PANoptosis-high group. The CoxBoost model, selected from 101 machine learning algorithms, exhibited stable prognostic performance across the TARGET and GEO cohorts, effectively stratifying patients into high-risk and low-risk groups. The high-risk group displayed worse survival outcomes, higher mutation frequencies, and distinct immune infiltration patterns, correlating with poorer prognosis and increased tumor purity.

Conclusion: This study provides novel insights into the PANoptosis landscape in OS and presents a validated prognostic model for risk stratification. The integration of scRNA-seq data with machine learning approaches enhances our understanding of OS heterogeneity and its impact on patient prognosis, offering potential avenues for targeted therapeutic strategies. Further validation in clinical settings is warranted to confirm the model’s utility in guiding personalized treatment for OS patients.

Background and Aims: Hepatocellular carcinoma (HCC) is a prevalent and aggressive liver cancer with high mortality rates. Sphingomyelin phosphodiesterase 3 (SMPD3) has recently been suggested to play an antitumor role in several cancers. This study is aimed at investigating the role of SMPD3 in HCC and its potential as a prognostic marker and therapeutic target.

Methods: A retrospective cohort study of HCC patients was conducted using clinical data from our hospital. Survival analyses, including Kaplan–Meier and multivariate Cox regression, were performed to assess the impact of SMPD3 expression on survival. Further analyses were carried out using data from The Cancer Genome Atlas (TCGA) HCC cohort. In vitro and in vivo experiments were conducted to evaluate the effects of SMPD3 overexpression on HCC cell lines and tumor growth in mice.

Results: High SMPD3 expression level was associated with improved survival in both our cohort and TCGA cohort. Multivariate Cox regression analysis confirmed high SMPD3 expression level as an independent predictor of better survival outcomes. In vitro and in vivo experiments demonstrated that SMPD3 overexpression significantly decreased HCC cell proliferation, migration, and invasion and inhibited tumor growth in a nude mouse model.

Conclusions: SMPD3 plays a protective role in HCC by inhibiting tumor growth and progression. Its high expression is associated with better survival outcomes and may serve as a promising prognostic marker and potential therapeutic target in HCC. Further research into the molecular mechanisms of SMPD3’s antitumor effects could lead to novel therapeutic strategies for HCC.

Objective: T cell exhaustion (TEX) is a critical determinant of immune resistance. This study was performed to investigate the key genes linked to TEX in cholangiocarcinoma (CCA) and construct a TEX-associated gene signature to forecast the prognosis of patients with CCA.

Methods: Based on the expression data acquired from the E-MTAB-6389 dataset, the TEX-related modules and module genes were identified using weighted coexpression network analysis (WGCNA). Subsequently, a TEX-related prognostic signature was built by using the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The immune cell infiltration in each CCA sample was evaluated using the single-sample gene set enrichment analysis (ssGSEA) package, followed by single-cell RNA sequencing (scRNA-seq) analysis. Furthermore, the expression of TEX-related genes in the gene signature was experimentally validated in CCA cells by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and western blot analysis.

Results: A total of 15 TEX-associated modules and 23 module genes were identified. Then, a four-gene signature related to TEX was established, containing Palladin, Cytoskeletal Associated Protein (PALLD), Member RAS Oncogene Family (RAB31), ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), and WISP1, which could predict prognosis of patients with CCA. Moreover, neutrophils, endothelial cells, B cells, and T cells exhibited significant infiltration in CCA samples, and these four TEX-related genes were both significantly positively correlated with T cells, endothelial cells, and B cells while negatively correlated with neutrophils. Moreover, a total of 13 cell types were annotated after scRNA-seq analysis. Notably, RAB31 was mainly highly expressed in monocytes, macrophages, DC2 (Dendritic Cells 2), and DC3 (Dendritic Cells 3), and PALLD, ADAMTS2, and WISP1 were mainly overexpressed in fibroblasts. Furthermore, experimental validation revealed that the expression levels of PALLD, RAB31, ADAMTS2, and WISP1 were consistent with the trend results of bioinformatics analysis.

Conclusion: A prognostic signature was developed by four TEX-related genes, including PALLD, RAB31, ADAMTS2, and WISP1, which might be a powerful predictor for the prognosis of patients with CCA. These TEX-related genes were related to the infiltration of neutrophils, endothelial cells, B cells, and T cells in CCA.

Taro (Colocasia esculenta (L.) Schott.) is an important edible and economically valuable crop that is also a source of high-quality starch. Its quality is determined by the content and proportion of amylopectin. Based on transcriptome sequencing of corms at different growth stages (T1–T6), 34,603 transcripts and 1727 novel genes with functional annotation were obtained. In total, 11,865 differentially expressed genes (DEGs) were identified among six development stages, with 3836 and 3404 DEGs in T2 versus T3 and T3 versus T4, respectively. The regulatory network of taro starch synthesis was constructed on the DeGNServer. Among three cloned soluble starch synthase (SS) genes, CeSS II might be the key gene responsible for soluble starch synthesis in taro corm. The putative transcription factor CeMyb108 might play a negative role in starch synthesis. Sanger sequencing CeSS II gene revealed a single nucleotide polymorphism (SNP) between two variety groups with high and low starch content. A kompetitive allele-specific PCR (KASP) marker, namely, CeSS II-SNP, was developed and validated in a natural population of 89 taro accessions. The starch content of the C:T group amounts to 517.45 mg/g, which is significantly (22.3%) higher than its counterpart (T:T). This newly developed marker is proved to be effective and would facilitate marker-assisted breeding for taro with high starch content.