Comparative and Functional Genomics最新文献

Background: Testicular seminomas, a common germ cell tumor, poses clinical challenges due to its molecular heterogeneity and limited biomarkers for precise diagnosis and prognosis. Leveraging multiomics approaches enables the comprehensive dissection of tumor complexity and facilitates the identification of key molecules influencing disease progression and therapeutic response.

Methods: Single-cell RNA transcriptomic sequencing (scRNA-seq) was utilized to explore the cellular and transcriptional heterogeneity of testicular seminomas. High-dimensional weighted gene coexpression network analysis (hdWGCNA) identified gene modules linked to tumor progression. Public datasets were integrated for gene expression and survival analyses, and drug sensitivity patterns were assessed using the GDSC database.

Results: scRNA-seq analysis revealed heterogeneous epithelial populations, with Epi1 cells exhibiting SLC5A5 and SPTBN4 as risk factors for advanced progression of seminomas. hdWGCNA identified nine gene modules, with the M6 module significantly enriched in Epi1 cells, implicating pathways such as negative regulation of ERAD and selective mRNA degradation. SPTBN4 was markedly upregulated in seminoma compared to nonseminomatous tumors and normal tissues, and its high expression was associated with poorer clinical outcomes and immunosuppressive microenvironments. Immune pathway analyses highlighted reduced antigen presentation and increased neutrophil extracellular trap (NET) formation in the SPTBN4-high group, suggesting diminished immunotherapeutic efficacy. Conversely, the SPTBN4-high group exhibited increased sensitivity to multiple chemotherapeutic agents, including thapsigargin and sorafenib, indicating its potential as a predictive marker for chemotherapy.

Conclusion: In conclusion, this multiomics study identifies SPTBN4 as a central biomarker in testicular seminomas, encompassing diagnostic, prognostic, and therapeutic dimensions. The integration of single-cell transcriptomics, hdWGCNA, and drug sensitivity analyses underscores the molecular complexity of seminomas and highlights the translational potential of SPTBN4 in guiding personalized treatment strategies. These findings provide a foundation for leveraging multiomics approaches to advance the clinical management of testicular seminomas and other heterogeneous malignancies.

Background: Clear cell renal cell carcinoma (ccRCC) is marked by aggressive characteristics and a poor prognosis. The involvement of KCNJ2, an inward rectifying potassium channel, in the progression of ccRCC, along with its potential roles in immune modulation and metabolic pathways, remains unclear.

Methods: The Cancer Genome Atlas (TCGA) database was utilized to analyze the gene expression, clinicopathological characteristics, and clinical relevance of KCNJ2. The prognostic value of KCNJ2 in ccRCC was evaluated with Kaplan–Meier survival analysis and receiver operating characteristic curve analyses. The TCGA-KIRC dataset was utilized to analyze tumor microenvironment (TME), focusing on tumor-infiltrating immune cells and immunomodulators. The biological functions of KCNJ2 were investigated in vitro using CCK-8, flow cytometry, wound healing, transwell, qRT-PCR, and Western blotting assays.

Results: KCNJ2 expression was notably higher in ccRCC than in normal kidney tissues, with increased levels associated with advanced tumor stages. However, KCNJ2 did not exhibit obvious prognostic value. Coexpression analysis identified associations with genes implicated in energy metabolism. Analysis of the TME and immune profile indicated a link between KCNJ2 expression and immune cell infiltration, along with particular immune checkpoints. In vitro studies demonstrated that KCNJ2 overexpression enhanced cell proliferation, migration, invasion, glucose production, and ATP generation.

Conclusion: KCNJ2 plays a crucial role in ccRCC progression through affecting glucose metabolism and immune responses. Our findings reveal the functional role of KCNJ2 in promoting tumor progression and metabolic reprogramming in ccRCC, highlighting its therapeutic potential as a novel target for ccRCC treatment. Further studies are essential to clarify the mechanisms by which KCNJ2 affects ccRCC biology and to evaluate its clinical relevance.

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.