ITIH1 suppresses carcinogenesis in renal cell carcinoma through regulation of the NF­κB signaling pathway.

Renal cell carcinoma (RCC) is a common malignancy of the urinary system. Although traditional therapies, such as surgery assisted with chemotherapy have improved the quality of life and survival time of patients with RCC, patients with metastasis or recurrence benefit little from such therapies. At present, little is known about the underlying mechanisms of RCC, rendering treatment selection and implementation challenging. Therefore, investigating the cause and underlying mechanisms of RCC remain of importance to explore potential new avenues for its treatment. Inter-α-trypsin inhibitor heavy chain 1 (ITIH1) is an inflammation-associated gene reported to suppress the progression of liver cancer. However, its role in RCC remains poorly understood. Therefore, the present study aimed to investigate the role and mechanism of ITIH1 in RCC. Based on data obtained from The Cancer Genome Atlas database, ITIH1 expression was demonstrated to be significantly higher in tumor tissues compared with normal tissues, which was in turn negatively associated with the survival of patients with RCC. However, in RCC cells, ITIH1 was shown to be expressed at significantly lower levels compared with those in HK-2 cells. The discrepancy between tissues and cell lines might be due to the different environment of cell growth. ITIH1 knockdown in RCC cells significantly increased cell proliferation and invasion whilst significantly decreasing the apoptosis rate, compared with those in control cells (without ITIH1 knockdown). By contrast, overexpression of ITIH1 significantly inhibited cell proliferation and invasion in RCC cells. In terms of western blotting results, the phosphorylation levels of NF-κB were significantly increased following ITIH1 knockdown. The protein expression level of IκB significantly decreased whereas that of IKK, Cyclin D1, proliferating cell nuclear antigen and α-smooth muscle actin were significantly increased in ITIH1-knockdown cells, compared with those in the control cells (without ITIH1 knockdown). This suggests that the NF-κB pathway may be activated after ITIH1 knockdown. Following treatment with the NF-κB pathway inhibitor JSH-23 in combination with ITIH1 knockdown, RCC cell proliferation and invasion were significantly reduced compared with those after ITIH1 knockdown alone. In summary, results from the present study suggest that ITIH1 can serve an inhibitory role in the progression of RCC, which could potentially be inhibited through the NF-κB signaling pathway.

Immune landscape of hepatocellular carcinoma: The central role of TP53-inducible glycolysis and apoptosis regulator.

Objective: This study aims to address the substantive issue of lacking reliable prognostic biomarkers in hepatocellular carcinoma (HCC) by investigating the relationship between TP53-inducible glycolysis and apoptosis regulator (TIGAR) and HCC prognosis using The Cancer Genome Atlas database. Methods: (1) Integrated statistical analyses, including logistic regression, Wilcoxon signed-rank test, and Kruskal-Wallis test, were conducted to explore the association between TIGAR expression and clinical-pathological features of HCC. (2) The Kaplan-Meier method combined with univariate and multivariate Cox regression models underscored TIGAR as a prognostic factor in HCC. (3) Gene set enrichment analysis (GSEA) revealed key pathways associated with TIGAR, while single-sample gene set enrichment analysis (ssGSEA) determined its relevance to cancer immune infiltration. Results: (1) Elevated TIGAR expression was significantly correlated with decreased survival outcomes in HCC patients. (2) GSEA highlighted the significant link between TIGAR and humoral immunity. (3) ssGSEA revealed a positive correlation between TIGAR expression and infiltration of Th1 and Th2 cells and a negative correlation with Th17 cell infiltration. Conclusion: TIGAR, as a potential prognostic biomarker for HCC, holds significant value in immune infiltration. Understanding the role of TIGAR could contribute to improved prognostic predictions and personalized treatment strategies for HCC patients.

Predictive role of oxidative stress-related genes in colon cancer: a retrospective cohort study based on The Cancer Genome Atlas.

PURPOSE: This study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer. MATERIALS AND METHODS: First, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)-(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan-Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms. RESULTS: A totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway. CONCLUSION: A new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.

Histology Atlas of the Developing Mouse Respiratory System From Prenatal Day 9.0 Through Postnatal Day 30.

Respiratory diseases are one of the leading causes of death and disability around the world. Mice are commonly used as models of human respiratory disease. Phenotypic analysis of mice with spontaneous, congenital, inherited, or treatment-related respiratory tract abnormalities requires investigators to discriminate normal anatomic features of the respiratory system from those that have been altered by disease. Many publications describe individual aspects of normal respiratory tract development, primarily focusing on morphogenesis of the trachea and lung. However, a single reference providing detailed low- and high-magnification, high-resolution images of routine hematoxylin and eosin (H&E)-stained sections depicting all major structures of the entire developing murine respiratory system does not exist. The purpose of this atlas is to correct this deficiency by establishing one concise reference of high-resolution color photomicrographs from whole-slide scans of H&E-stained tissue sections. The atlas has detailed descriptions and well-annotated images of the developing mouse upper and lower respiratory tracts emphasizing embryonic days (E) 9.0 to 18.5 and major early postnatal events. The selected images illustrate the main structures and events at key developmental stages and thus should help investigators both confirm the chronological age of mouse embryos and distinguish normal morphology as well as structural (cellular and organ) abnormalities.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients.

OBJECTIVE: The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear. METHODS: The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored. RESULTS: A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes. CONCLUSION: Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

Atlas of metastatic gastric cancer links ferroptosis to disease progression and immunotherapy response.

BACKGROUND & AIMS: Metastases from gastric adenocarcinoma (GAC) lead to high morbidity and mortality. Developing innovative and effective therapies requires a comprehensive understanding of the tumor and immune biology of advanced GAC. Yet, collecting matched specimens from advanced, treatment-naïve GAC patients poses a significant challenge, limiting the scope of current research, which has predominantly focused on localized tumors. This gap hinders a deeper insight into the metastatic dynamics of GAC. METHODS: We performed in-depth single-cell transcriptome and immune profiling on 68 paired, treatment-naïve, primary-metastatic tumors to delineate alterations in cancer cells and their tumor microenvironment (TME) during metastatic progression. To validate our observations, we conducted comprehensive functional studies both in vitro and in vivo, employing cell lines, multiple PDX and novel mouse models of GAC. RESULTS: Liver and peritoneal metastases exhibited distinct properties in cancer cells and dynamics of TME phenotypes, supporting the notion that cancer cells and their local TMEs co-evolve at metastatic sites. Our study also revealed differential activation of cancer meta-programs across metastases. We observed evasion of cancer cell ferroptosis via GPX4 upregulation during GAC progression. Conditional depletion of Gpx4 or pharmacological inhibition of ferroptosis resistance significantly attenuated tumor growth and metastatic progression. Additionally, ferroptosis-resensitizing treatments augmented the efficacy of CAR T-cell therapy. CONCLUSIONS: This study represents the largest single-cell dataset of metastatic GACs to date. High-resolution mapping of the molecular and cellular dynamics of GAC metastasis has revealed a rationale for targeting ferroptosis defense in combination with CAR T-cell therapy as a novel therapeutic strategy with potential immense clinical implications.

The Effectiveness of Electronic Cannulation Atlas in Patients with Arteriovenous Fistula.

Objective: This study aimed to assess the effectiveness of an electronic cannulation atlas in preventing and treating complications of arteriovenous fistula. Methods: The observation group, consisting of 92 dialysis patients from July to December 2021, was managed with an electronic cannulation atlas. After 6 months, the incidence of complications such as stenosis, hematoma, thrombus, aneurysm, and cannulation failure was compared between the groups. Nurse satisfaction with the electronic cannulation atlas system was also assessed through a questionnaire. Results: The observation group had lower incidence rates of arteriovenous fistula stenosis, thrombus, aneurysm, and failure rate of cannulation compared to the control group, with statistically significant differences (p<0.05). The incidence rates of hematoa were similar in both groups, showing no significant difference (p>0.05). After 3 months of management, the incidence of arteriovenous fistula complications in the observation group was significantly lower than in the control group (p<0.05). Additionally, utilizing the electronic cannulation atlas system was found to increase nurses' job satisfaction. Conclusion: The use of electronic cannulation atlas for the treatment of patients' arteriovenous fistula could effectively reduce the incidence of complications of patients' arteriovenous fistula, reduce the failure rate of cannulation, reduce the workload of nurses, and improve the job satisfaction of nurses.

LiverSCA: A comprehensive and user-friendly cell atlas in human hepatocellular carcinoma.

We developed a cell atlas named LiverSCA on human liver cancer single-cell RNA sequencing data. It has a user-friendly web interface and comprehensive functionalities aiming to help researchers to make easy access to cellular and molecular landscapes of the tumor microenvironment in liver cancer. LiverSCA includes a complete analytical pipeline that allow mechanistic exploration on a wide variety of functionalities, such as cell clustering, cell annotation, identification of differentially expressed genes, functional enrichment analysis, analysis of cellular crosstalk, and pseudo-time trajectory analysis. Notably, our intuitive web interface allows users, particularly wet-lab researchers, to easily explore and undertake data discovery, without the need to handle any of the raw data.

Genes associated with cortical thickness alterations in behavioral addiction.

Behavioral addiction (BA) is a conceptually new addictive phenotype characterized by compulsive reward-seeking behaviors despite adverse consequences. Currently, its underlying neurogenetic mechanism remains unclear. Here, this study aimed to investigate the association between cortical thickness (CTh) and genetic phenotypes in BA. We conducted a systematic search in five databases and extracted gene expression data from the Allen Human Brain Atlas. Meta-analysis of 10 studies (343 addicted individuals and 355 controls) revealed that the BA group showed thinner CTh in the precuneus, postcentral gyrus, orbital-frontal cortex, and dorsolateral prefrontal cortex (P < 0.005). Meta-regression showed that the CTh in the precuneus and postcentral gyrus were negatively associated with the addiction severity (P < 0.0005). More importantly, the CTh phenotype of BA was spatially correlated with the expression of 12 genes (false discovery rate [FDR] < 0.05), and the dopamine D2 receptor had the highest correlation (rho = 0.55). Gene enrichment analysis further revealed that the 12 genes were involved in the biological processes of behavior regulation and response to stimulus (FDR < 0.05). In conclusion, our findings demonstrated the thinner CTh in cognitive control-related brain areas in BA, which could be associated with the expression of genes involving dopamine metabolism and behavior regulation.

Functional magnetic resonance imaging alternations in suicide attempts individuals and their association with gene expression.

BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) has shown brain activity alterations in individuals with a history of attempted suicide (SA) who are diagnosed with depression disorder (DD) or bipolar disorder (BD). However, patterns of spontaneous brain activity and their genetic correlations need further investigation. METHODS: A voxel-based meta-analysis of 19 studies including 26 datasets, involving 742 patients with a history of SA and 978 controls (both nonsuicidal patients and healthy controls) was conducted. We examined fMRI changes in SA patients and analyzed the association between these changes and gene expression profiles using data from the Allen Human Brain Atlas by partial least squares regression analysis. RESULTS: SA patients demonstrated increased spontaneous brain activity in several brain regions including the bilateral inferior temporal gyrus, hippocampus, fusiform gyrus, and right insula, and decreased activity in areas like the bilateral paracentral lobule and inferior frontal gyrus. Additionally, 5,077 genes were identified, exhibiting expression patterns associated with SA-related fMRI alterations. Functional enrichment analyses demonstrated that these SA-related genes were enriched for biological functions including glutamatergic synapse and mitochondrial structure. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons cells, and during early developmental periods. CONCLUSION: Our findings suggest a neurobiological basis for fMRI abnormalities in SA patients with DD or BD, potentially guiding future genetic and therapeutic research.

Compressed representation of brain genetic transcription.

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

ATLAS: Mapping ATtention's Location And Size to probe five modes of serial and parallel search.

Conventional visual search tasks do not address attention directly and their core manipulation of 'set size' - the number of displayed items - introduces stimulus confounds that hinder interpretation. However, alternative approaches have not been widely adopted, perhaps reflecting their complexity, assumptions, or indirect attention-sampling. Here, a new procedure, the ATtention Location And Size ('ATLAS') task used probe displays to track attention's location, breadth, and guidance during search. Though most probe displays comprised six items, participants reported only the single item they judged themselves to have perceived most clearly - indexing the attention 'peak'. By sampling peaks across variable 'choice sets', the size and position of the attention window during search was profiled. These indices appeared to distinguish narrow- from broad attention, signalled attention to pairs of items where it arose and tracked evolving attention-guidance over time. ATLAS is designed to discriminate five key search modes: serial-unguided, sequential-guided, unguided attention to 'clumps' with local guidance, and broad parallel-attention with or without guidance. This initial investigation used only an example set of highly regular stimuli, but its broader potential should be investigated.

Ponticulus posticus and skeletal malocclusion: A pilot study in a Southern Italian pre-orthodontic court.

Purpose: Ponticulus posticus (PP) is a bony protrusion located between the posterior portion of the superior articular process and the posterolateral portion of a posterior arch of the atlas vertebrae in the cervical spine. The aim of this study is to verify the presence of different types of PP in a Southern Italian pre-orthodontic cohort to understand its correlation with skeletal class and maturity. Methods: A case-control retrospective study was conducted, utilizing 212 latero-lateral telecranium radiographs to analyze skeletal maturity according to the cervical vertebral maturation method, the Angle's classification of malocclusion (I, II, or III), and the presence or absence of the PP, whether complete (c-PP) or partial (p-PP). A total of 212 lateral cephalograms were analyzed. Results: Of the 72 male patients, 67 (93%) exhibited PP, and 116 (88%) were PP. The chi-square value was 0.001, while Cramer's V was 0.270, indicating a significant correlation between age groups and PP presence, and a very strong association overall. Out of the 41 complete PP cases, class I was notably more prevalent than classes II and III. Conclusion: Orthodontists should carefully consider PP when assessing and treating individuals with or without skeletal discrepancies and dental anomalies.

CDKL3 is a promising biomarker for diagnosis and prognosis prediction in patients with hepatocellular carcinoma.

Cyclin-dependent kinase-like 3 (CDKL3) has been identified as an oncogene in certain types of tumors. Nonetheless, its function in hepatocellular carcinoma (HCC) is poorly understood. In this study, we conducted a comprehensive analysis of CDKL3 based on data from the HCC cohort of The Cancer Genome Atlas (TCGA). Our analysis included gene expression, diagnosis, prognosis, functional enrichment, tumor microenvironment and metabolic characteristics, tumor burden, mRNA expression-based stemness, alternative splicing, and prediction of therapy response. Additionally, we performed a cell counting kit-8 assay, TdT-mediated dUTP nick-end Labeling staining, migration assay, wound healing assay, colony formation assay, and nude mouse experiments to confirm the functional relevance of CDKL3 in HCC. Our findings showed that CDKL3 was significantly upregulated in HCC patients compared to controls. Various bioinformatic analyses suggested that CDKL3 could serve as a potential marker for HCC diagnosis and prognosis. Furthermore, CDKL3 was found to be involved in various mechanisms linked to the development of HCC, including copy number variation, tumor burden, genomic heterogeneity, cancer stemness, and alternative splicing of CDKL3. Notably, CDKL3 was also closely correlated with tumor immune cell infiltration and the expression of immune checkpoint markers. Additionally, CDKL3 was shown to independently function as a risk predictor for overall survival in HCC patients by multivariate Cox regression analysis. Furthermore, the knockdown of CDKL3 significantly inhibited cell proliferation in vitro and in vivo, indicating its role as an oncogene in HCC. Taken together, our findings suggest that CDKL3 shows promise as a biomarker for the detection and treatment outcome prediction of HCC patients.

A pilot study for the evaluation of 3D anatomy application 'Complete Anatomy' as an additional non-mandatory study tool for the functional anatomy classes of first year physical therapy students.

For this pilot study, the use of the digital 3D anatomy application Complete Anatomy was evaluated as a non-mandatory additional study tool throughout the semester. The aim of the study was to investigate if the Complete Anatomy usage time had an effect on final exam grades and how the app was used post-Covid19. This cross-sectional study asked first year bachelor/freshman university students of Physical Therapy and Rehabilitation Sciences to fill out a questionnaire to gauge Complete Anatomy usage time, the student's exam results, and how the app was used, for example, in relation to other study tools. A positive correlation was found between the proportion of students that passed the final exam and the number of hours of Complete Anatomy usage (rs (4) = 0.94, p = 0.016). Compared to students who didn't use Complete Anatomy, these positive effects were observed when students used Complete Anatomy for at least 10 h (p = 0.04). The app was well-used with almost half of the respondents reporting >10 h of usage time. The results from this study provide a good overview of how and how often Complete Anatomy is used. Although a positive correlation between the hours of use and the number of passing grades was found in this pilot study, a future study to prove the causality between these two factors is warranted to further investigate the effect of Complete Anatomy as an additional non-mandatory study tool.

Development of a genome atlas for discriminating benign, preinvasive, and invasive lung nodules.

To tackle misdiagnosis in lung cancer screening with low-dose computed tomography (LDCT), we aimed to compile a genome atlas for differentiating benign, preinvasive, and invasive lung nodules and characterize their molecular pathogenesis. We collected 432 lung nodule tissue samples from Chinese patients, spanning benign, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). We performed comprehensive sequencing, examining somatic variants, gene expressions, and methylation levels. Our findings uncovered EGFR and TP53 mutations as key drivers in - early lung cancer development, with EGFR mutation frequency increasing with disease progression. Both EGFR mutations and EGF/EGFR hypo-methylation activated the EGFR pathway, fueling cancer growth. Transcriptome analysis identified four lung nodule subtypes (G1-4) with distinct molecular features and immune cell infiltrations: EGFR-driven G1, EGFR/TP53 co-mutation G2, inflamed G3, stem-like G4. Estrogen/androgen response was associated with the EGFR pathway, proposing a new therapy combining tyrosine kinase inhibitors with antiestrogens. Preinvasive nodules exhibited stem cell pathway enrichment, potentially hindering invasion. Epigenetic regulation of various genes was essential for lung cancer initiation and development. This study provides insights into the molecular mechanism of neoplastic progression and identifies potential diagnostic biomarkers and therapeutic targets for lung cancer.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

Identification and validation of a novel 17 coagulation-related genes signature for predicting prognostic risk in colorectal cancer.

Background: Patients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients' prognosis. Methods: Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model's predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients. Result: Eight hundred forty-five genes were obtained by searching the theme term "coagulation" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction. Conclusion: The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.

Analysis of methylation-driven genes for predicting the prognosis of patients with oral squamous cell carcinoma.

Background: Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy that is characterized by early distant metastasis and poor prognosis. DNA methylation plays an important role in the etiology and pathogenesis of OSCC. This study aimed to identify methylation-driven genes through bioinformatics analysis as potential biomarkers for early diagnosis and prognostic assessment of OSCC. Methods: Methylation data, RNA sequencing (RNA-seq) data and clinical prognosis information of OSCC patients were retrieved from The Cancer Genome Atlas (TCGA) database. The R packages MethylMix were employed to analyze the correlation between methylation status and corresponding gene expression in tumor and normal tissues to obtain methylation-driven genes. Univariate Cox regression analysis was developed to further screen methylation-driven genes associated with the prognosis of OSCC patients. Subsequently, multivariate Cox regression analysis was utilized to construct a linear prognostic risk prediction model. Furthermore, a combined survival analysis integrating methylation and gene expression was performed to investigate the prognostic value. Results: A total of 374 differentially expressed methylation-driven genes were identified. Seven methylation-driven genes (BST2, KRT15, ZNF134, NT5E, GSTA7P, NAPRT, and GOLPH3L) were found to be significantly associated with patient prognosis. Additionally, four methylation-driven genes (BST2, KRT15, ZNF134 and NAPRT) were used to construct a linear prognostic risk prediction model for OSCC patients. Furthermore, a combined Kaplan-Meier survival analysis revealed that three methylation-driven genes (ZKSCAN7, MFF, ZNF134) alone can be used as independent prognostic markers or drug targets. Conclusions: Our findings facilitate a better understanding of molecular mechanisms of OSCC and provide potential biomarkers of early diagnosis, precision treatment and prognosis evaluation.