LASSO Cox Regression Research Articles

GG-NER’s role in androgen receptor signaling inhibitor response for advanced prostate cancer

BackgroundAdvanced prostate cancer (PCa) often initially responds to androgen receptor signaling inhibitors (ARSI) but frequently develops resistance, driven by tumor heterogeneity and therapeutic pressure. Addressing the clinical challenge of identifying non-responsive patients and discovering new therapeutic targets is urgently needed.MethodsWe utilized single-sample gene set enrichment analysis (ssGSEA) to elucidate the influence of the GG-NER pathway on ARSI response in PCa. We then constructed and validated a prognostic model based on this pathway using LASSO regression, Kaplan-Meier analysis, Cox regression, and ROC analysis. Additionally, we mapped tumor mutations to delineate the mutational landscapes across different risk groups and explored functional pathways through GO, KEGG, and GSEA analyses. The impact of the GG-NER pathway on enzalutamide sensitivity and DNA repair in PCa was further validated through CCK-8 assays, colony formation assays, in vivo experiments, and immunofluorescence.ResultsssGSEA indicated a trend of GG-NER pathway upregulation in patients with poor ARSI response. The GG-NER characteristic gene score (NECGS) identified a high-risk group with diminished ARSI response, serving as an independent prognostic indicator with strong predictive power. This high-risk group exhibited elevated TP53 mutation frequencies and significant enrichment in key pathways such as ribosome and mitochondrial functions, as well as MYC and E2F signaling. Experimental validation confirmed that targeting the GG-NER pathway or its key gene, ACTL6A, significantly reduces enzalutamide resistance in resistant cell lines and increases γH2AX expression.ConclusionNECGS effectively predicts ARSI response in PCa, and our comprehensive analysis underscores the critical role of the GG-NER pathway in enzalutamide resistance, positioning ACTL6A as a potential therapeutic target for PCa.

Integration of single-cell and bulk analysis reveals TBXAS1 as a key platelet-related gene causing poor prognosis in osteosarcoma

BackgroundPlatelets are associated with poor prognosis in most tumors, but their specific pathogenic mechanism in osteosarcoma is not yet clear. The objective of this study is to conduct an in-depth analysis of how genes closely related to platelet function impact the prognosis of osteosarcoma patients. We hope that through this research, we can uncover the potential mechanisms of these genes in the development and progression of osteosarcoma, thereby providing new therapeutic strategies and theoretical foundations for improving the prognosis of osteosarcoma patients.MethodWe collected the blood routine test data of patients who were initially diagnosed with osteosarcoma at the Department of Bone Tumors, West China Hospital, from January 2012 to January 2022. By applying the LASSO-COX regression analysis, a statistical method, we found that the platelet count is associated with the prognosis of osteosarcoma patients. To further explore this relationship, we obtained single-cell data and bulk RNA data of osteosarcoma patients from the TARGET database and GEO database, respectively. By analyzing these data, we revealed at the transcriptomic level how platelets contribute to the poor prognosis in osteosarcoma patients.ResultPlatelets are associated with the prognosis of osteosarcoma patients (HR = 3.9, 95% CI = 1.9–8.1, P < 0.001). Through the analysis of transcriptomic data from the TARGET database and GEO database, we found significant heterogeneity in tumor-specific pathways and immune infiltration under different platelet-related gene expression patterns. Among these, TBXAS1 was identified as a key gene that affects the prognosis of osteosarcoma patients. In addition, single-cell data analysis showed that the platelet-related gene TBXAS1 is mainly enriched in macrophages, and markers of macrophages are significantly associated with poor prognosis in osteosarcoma patients.ConclusionTBXAS1 is a key platelet-related gene that leads to poor prognosis in osteosarcoma, and this gene may affect the prognosis of osteosarcoma patients by interacting with macrophages.

CRISPR-Cas9 screening identified novel subtypes of cutaneous melanoma based on essential cancer genes.

Our primary objective was to identify genes critical for cutaneous melanoma (CM) and related typing, based on essential genes, to generate novel insights for clinical management and immunotherapy of patients with CM. We analyzed RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and sequencing data of 29 CM cell line from Cancer Cell Line Encyclopedia (CCLE) databases. Combined with DepMap database, 406 CM essential cancer genes were finally obtained. Based on the expression of essential genes in cancer, the patients included in TCGA and Gene Expression Omnibus (GEO) databases were divided into three different molecular subtypes (C1, C2, and C3) by the NMF algorithm. Data analysis from TCGA and GEO datasets revealed that subtype C3 had the poorest prognosis, while subtype C1 exhibited the best prognosis. Combined with the CIBERSORT, ESTIMATE and ssGSEA algorithm, patients with different molecular subtypes can be divided into two immune subtypes (hot and cold). We found that subtype C1 was characterized by hot tumors, in contrast to subtypes C2 and C3, which were characterized by cold tumors. Then, we used univariate Cox regression, LASSO, and multifactor Cox regression analysis to select risk genes and constructed a prognostic model based on eight genes: RABIF, CDCA8, FOXM1, SPRR2E, AIP, CAP1, CTSW, and IFITM3. All patients were divided into two risk subtypes (high and low ) according to the median of risk scores. We found that most hot tumor subtypes were found in the low-risk subtypes and most patients with this subtype survived for longer. Ultimately, we selected RABIF, which exhibits the highest risk coefficient, for histological and cytological verification. The results showed that RABIF was overexpressed in melanoma. Inhibition of RABIF expression could suppress the proliferation and invasion of melanoma cells and promote the apoptosis of melanoma cells. In conclusion, we used CRISPR-Cas9 screening to verify the association between molecular subtypes (C1, C2, and C3), immune subtypes (hot and cold), and risk subtypes (high and low) in patients with CM, particularly in distinguishing survival and prognosis. These findings can be used to guide clinical management and immunotherapy of patients with CM.

Development and application of a predictive model for survival and drug therapy based on COVID-19-related lncRNAs in non-small cell lung cancer.

Numerous studies have substantiated the pivotal role of long non-coding RNAs (lncRNAs) in the progression of non-small cell lung cancer (NSCLC) and the prognosis of afflicted patients. Notably, individuals with NSCLC may exhibit heightened vulnerability to the novel coronavirus disease (COVID-19), resulting in a more unfavorable prognosis subsequent to infection. Nevertheless, the impact of COVID-19-related lncRNAs on NSCLC remains unexplored. The aim of our study was to develop an innovative model that leverages COVID-19-related lncRNAs to optimize the prognosis of NSCLC patients. Pertinent genes and patient data were procured from reputable databases, including TCGA, Finngen, and RGD. Through co-expression analysis, we identified lncRNAs associated with COVID-19. Subsequently, we employed univariate, LASSO, and multivariate COX regression techniques to construct a risk model based on these COVID-19-related lncRNAs. The validity of the risk model was assessed using KM analysis, PCA, and ROC. Furthermore, functional enrichment analysis was conducted to elucidate the functional pathways linked to the identified lncRNAs. Lastly, we performed TME analysis and predicted the drug sensitivity of the model. Based on risk scores, patients were categorized into high- and low-risk subgroups, revealing distinct clinicopathological factors, immune pathways, and chemotherapy sensitivity between the subgroups. Four COVID-19-related lncRNAs (AL161431.1, AC079949.1, AC123595.1, and AC108136.1) were identified as potential candidates for constructing prognostic prediction models for NSCLC. We also observed a positive correlation between risk score and MDSC, exclusion, and CAF. Additionally, two immune pathways associated with high-risk and low-risk subgroups were identified. Our findings further support the association between COVID-19 infection and neuroactive ligand-receptor interaction, as well as steroid metabolism in NSCLC. Moreover, we identified several highly sensitive chemotherapy drugs for NSCLC treatment. The developed model holds significant value in predicting the prognosis of NSCLC patients and guiding treatment decisions.

Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer.

To reveal the clinical value of cuproptosis-related genes on prognosis and metastasis in non-small cell lung cancer. Gene expression profiles and clinical information of non-small cell lung cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The data were grouped into training set, internal testing set, and external testing set. A risk prognostic model was constructed by Lasso-Cox regression analysis. Hub genes were identified and evaluated using immunohistochemistry and the transwell migration assay in 50 clinical patients. A total of 17/19 cuproptosis-related genes were differentially expressed in tumors, 8 were significantly associated with prognosis, and 4 were markedly associated with metastasis. A risk model based on 2 cuproptosis-related genes was constructed and validated for predicting overall survival. The risk score was proven to be an independent risk factor for the prognosis of non-small cell lung cancer. Dihydrolipoamide S-acetyltransferase and dihydrolipoamide S-succinyltransferase, key genes in cuproptosis, were proven to be associated with non-small cell lung cancer prognosis and metastasis. Immunohistochemistry showed that their expression significantly predicted metastasis but failed to predict prognosis in non-small cell lung cancer patients. The transwell migration assay further increased the cellular reliability of our findings. The cuproptosis-related genes prognostic model effectively predicted the prognosis of non-small cell lung cancer. Dihydrolipoamide S-acetyltransferase and dihydrolipoamide S-succinyltransferase may serve as predictive markers for metastasis in non-small cell lung cancer.

Integrative analysis of a novel immunogenic PANoptosis‑related gene signature in diffuse large B-cell lymphoma for prognostication and therapeutic decision-making

This study aimed to develop a PANoptosis-related gene prognostic index (PANGPI) to explore its potential value in predicting the prognosis and immunotherapy response of diffuse large B-cell lymphoma (DLBCL). Differentially expressed genes of DLBCL from GEO databases were analyzed and mapped to the PANoptosis gene set. The independent prognostic value of the PANGPI signature was evaluated using LASSO Cox regression and multivariate Cox regression. Additionally, the tumor infiltrating lymphocyte (TIL) characteristics and mutation landscape of both subgroups were evaluated, and drug sensitivity was predicted using the GDSC database. Furthermore, in silico docking and molecular dynamic simulation studies were presented to elucidate the mode of interaction of these predicted drugs. The PANGPI risk score was an independent risk factor for the prognosis of patients with DLBCL and exhibited good prognostic predictive performance. Furthermore, the cytolytic activity of the TILs was positively correlated with the PANGPI scores. Additionally, the PANGPI enabled the identification of 3 chemotherapeutic agents, including BMS-536924, Gefitinib, Navitoclax for DLBCL patients in the high-risk group. We established a novel PANoptosis-related gene subtyping system in DLBCL, which could shed a novel light on the development of new biomarkers for DLBCL.

Unraveling molecular signatures and prognostic biomarkers in glioblastoma: a comprehensive study on treatment resistance and personalized strategies

BackgroundGlioblastoma (GBM) is a highly aggressive primary brain tumor with limited treatment success and poor prognosis. Despite surgical resection and adjuvant therapies, GBM often recurs, and resistance to radiotherapy and temozolomide presents significant challenges. This study aimed to elucidate molecular signatures associated with treatment responses, identify potential biomarkers, and enhance personalized treatment strategies for GBM.MethodsWe conducted a comprehensive analysis using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The GEO dataset (GSE206225) was used to identify differentially expressed genes (DEGs) between radiation-sensitive/resistant and temozolomide-sensitive/resistant GBM samples. TCGA data were utilized for subsequent analyses, including Lasso-Cox regression, risk score model construction, Kaplan–Meier survival analysis, and gene set enrichment analysis (GSEA). Hub genes were identified through survival analysis, and a gene prognostic nomogram was developed. Additionally, validation of the three-gene risk signature through multiple external cohorts and validation of protein expression levels were performed.ResultsDEG analysis identified 111 genes associated with chemoradiotherapy resistance, providing insights into the complex landscape of GBM treatment response. The risk score model effectively stratified patients, showing significant differences in overall survival and progression-free survival. GSEA offered a deeper understanding of pathway activities, emphasizing the intricate molecular mechanisms involved. NNAT, IGFBP6, and CYGB were identified as hub genes, and a gene prognostic nomogram demonstrated predictive accuracy.ConclusionThis study sheds light on the molecular intricacies governing GBM treatment response. The identified hub genes and the gene prognostic nomogram offer valuable tools for predicting patient outcomes and guiding personalized treatment strategies. These findings contribute to advancing our understanding of GBM biology and may pave the way for improved clinical management.

Diverse RNA methylation patterns in neutrophils: key drivers in hepatocellular carcinoma.

Neutrophils, crucial in the immune system, have recently been implicated in promoting malignancy. RNA methylation, an essential epigenetic feature, plays a key role in tumor microenvironment (TME) reprogramming. However, the relationship between neutrophils and RNA methylation in hepatocellular carcinoma (HCC) remains unclear. We analyzed single-cell sequencing data from HCC, focusing on cell subtype and TME construction. RNA methylation "writers" were selected, and their expression in neutrophils was evaluated. Two neutrophil subtypes (high/low RNA methylation) were identified. Differentially expressed genes (DEGs) between these subtypes were confirmed, leading to the identification of 6 molecular subtypes via consensus clustering. A prognostic scoring system was developed using LASSO Cox regression, resulting in a novel neutrophil RNA methylation (NRM) scoring system to assess TME heterogeneity and clinical features. TRPM3, specifically expressed in HCC-infiltrating neutrophils, may regulate RNA modification in tumor pathogenesis. HCC patients were stratified into low/high-NRM score groups, further refined into an advanced NRM (a-NRM) score by incorporating lncRNA data. High a-NRM scores correlated with advanced TNM stage, higher pathological grade, and increased suppressive immune cells. A nomogram incorporating the a-NRM score demonstrated a concordance index indicative of good predictive performance. The a-NRM score is a reliable predictor of prognosis and could guide treatment selection in HCC patients, enhancing clinical response to immunotherapy. TRPM3 also presents as a potential therapeutic target in HCC.

Identification and validation of lung adenocarcinoma (LUAD)-associated targets for monascin from the extracts of Monascus purpureus-fermented rice: Compound preparation, high-throughput genome sequencing and bioinformatics analysis

Despite the remarkable bioactivities exhibited by monascin against several tumors, its therapeutic targets remain unexplored. In this study, our objective is to identify therapeutic targets based on the antitumor activity of monascin against lung adenocarcinoma (LUAD) A549 cells. The compound monascin was derived from extracts of Monascus purpureus-fermented rice. Its chemical structure was determined using spectroscopic methods, while the physicochemical properties of monascin were investigated through thermal behavior analysis. Herein, antitumor studies indicated that monascin significantly inhibited the viability of A549 cells, with an IC50 value of 2.05 μM; RNA-sequencing revealed 12 up-regulated and 29 down-regulated genes as differentially expressed genes (DEGs) for A549 cells in response to monascin; RT-qPCR validation supported the plausibility of the RNA-sequencing results. Moreover, utilizing data from the Cancer Genome Atlas (TCGA) database, univariate and multivariate Cox regression analyses provided evidence supporting GPR37 and FAM83A as potential candidate genes for predicting disease progression in LUAD patients. Additionally, the LASSO Cox regression and nomogram analyses confirmed that FAM83A and GPR37 genes had the potential to predict overall survival for LUAD patients. Finally, molecular docking studies suggested that monascin could interact with both GPR37 and FAM83A proteins through the hydrogen bonding and hydrophobic interactions; Western blotting assays indicated that monascin inhibited the expression levels of both GPR37 and FAM83A proteins. Overall, the clinical prognosis of GPR37 and FAM83A highlights the rationale for targeting these specific genes with monascin. Additionally, there findings provide compelling evidence for the ploypharmacological properties of monascin against LUAD.

Decoding the Ferroptosis-Related Gene Signatures and Immune Infiltration Patterns in Ovarian Cancer: Bioinformatic Prediction Integrated with Experimental Validation.

Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer. In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression. According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group's OS was markedly shorter than that of low-risk group (Log-rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis. We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.

TGF-β Score based on Silico Analysis can Robustly Predict Prognosis and Immunological Characteristics in Lower-grade Glioma: The Evidence from Multicenter Studies.

Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown. We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation. In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812). In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

Development of a novel centrosome-related risk signature to predict prognosis and treatment response in lung adenocarcinoma

BackgroundAbnormalities of centrosomes, the major microtubular organizing centers of animal cells and regulators of cell cycle progression, usually accelerate tumor progression, but their prognostic value in lung adenocarcinoma (LUAD) remains insufficiently explored.MethodsWe collected centrosome genes from the literature and identified LUAD-specific centrosome-related genes (CRGs) using the single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA). Univariate Cox was performed to screen prognostic CRGs. Consistent clustering was performed to classify LUAD patients into two subgroups, and centrosome-related risk score signatures were constructed by Lasso and multivariate Cox regression to predict overall survival (OS). We further explored the correlation between CRS and patient prognosis, clinical manifestations, mutation status, tumor microenvironment, and response to different treatments.ResultsWe constructed centrosome-associated prognostic features and verified that CRS could effectively predict 1-, 3-, and 5-year survival in LUAD patients. In addition, patients in the high-risk group exhibited elevated tumor mutational loads and reduced levels of immune infiltration, particularly of T and B cells. Patients in the high-risk group were resistant to immunotherapy and sensitive to 5-fluoropyrimidine and gefitinib. The key gene spermine synthase (SRM) is highly expressed at the mRNA and protein levels in LUAD.DiscussionOur work develops a novel centrosome-related prognostic signature that accurately predicts OS in LUAD and can assist in clinical diagnosis and treatment.

The methylation signature of hepatocellular carcinoma trajectory based on pseudotime and chronological time for predicting precancerous patients.

Early screening of hepatocellular carcinoma (HCC) is strongly recommended for hepatitis B virus (HBV)-infected patients. We aimed to develop and validate a predictive nomogram based on HCC occurrence trajectory for screening precancerous patients with HCC. Peripheral blood mononuclear cells (PBMC) samples from 22 patients with HCC with their precancerous stage (n = 55) and 18 healthy controls were measured using HumanMethylation EPIC BeadChip assay. HCC trajectory was assessed by pseudotime based on TimeAx algorithm and chronological time. The 43 candidate CpG sites were selected from the methylation signature and measured using multiplex bisulfite sequencing in a retrospective cohort of HBV-infected patients (n = 604). A 5-CpG-classifier was built using the LASSO Cox regression model, based on the association between the methylation level of every CpG and the duration from enrollment to HCC occurrence of individual patient. We validated the risk stratification and predictive accuracy of this classifier in both the primary cohort (n = 300) and independent validation cohort (n = 304). Pseudotime and chronological time of HCC trajectory analysis revealed that the PD-1/PD-L1 pathway underwent changes in the precancerous stage. Based on the trajectory of methylation signature, we built a 5-CpG-classifier which remained powerful and independent predictive efficiency after stratified analysis by clinicopathological risk factors in both primary cohort and independent validation cohort. A predicting nomogram including the 5-CpG-classifier was constructed after multivariate analysis. One-year cumulative hazard of HCC in low- and high-risk groups of HBV-infected patients was 3.0% (0.1%-5.8%) and 17.90% (11.00%-24.3%) (P < .0001) in primary cohort, 4.5% (1.20%-7.80%) and 27.3 (18.90-34.90) (P < .0001) in the independent validation cohort. One-year before HCC was a critical period of transitional time when parts of the methylation profile underwent shifting toward HCC like. The nomogram could identify precancerous stage patients with HCC who should be screened for early diagnosis and intervention.

A novel necroptosis-related genes signature to predict prognosis and treatment response in bladder cancer.

Necroptosis, a form of programmed inflammatory cell death, plays a crucial role in tumor development, necrosis, metastasis, and immune response. This study aimed to explore the role of necroptosis in BLCA and construct a new prognostic model to guide clinical treatment and predict individualized treatment response. The transcriptome profiling and the corresponding clinical data of BLCA patients were obtained from the Cancer Genome Atlas database (TCGA) and GEO databases. Univariate, multivariate and LASSO Cox regression analyses were used to identify and construct prognostic features associated with necroptosis. We constructed and validated a prognostic model associated with the patient's overall survival (OS). A nomogram was established to predict the survival rates of BLCA patients. Finally, the correlation between risk scores and tumor immune microenvironment, somatic mutations, immunotherapy, and chemotherapy was comprehensively analyzed. The study found two distinct NRG clusters and three gene subtypes, with significant differences in pathway enrichment and immune cell infiltration associated with different NRG clusters in the TME. In addition, we screened out six necroptosis prognosis-related genes (including PPP2R3A; CERCAM; PIK3IP1; CNTN1; CES1 and CD96) to construct a risk score prognostic model. Significant differences in overall survival rate, immune cell infiltration status, and somatic mutations existed between the high and low-risk scores in BLCA patients. Finally, drug sensitivity analysis showed that high-risk patients benefited more from immunotherapy and chemotherapy drugs. This study explores the importance of necroptosis in the prognosis of patients with BLCA, and the prognostic features associated with necroptosis that we identified can serve as new biomarkers to help develop more precise treatment strategies.

Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer

The aim of this study was to develop a quantitative feature-based model from histopathologic images to assess the prognosis of patients with gastric cancer. Whole slide image (WSI) images of H&E-stained histologic specimens of gastric cancer patients from The Cancer Genome Atlas were included and randomly assigned to training and test groups in a 7:3 ratio. A systematic preprocessing approach was employed as well as a non-overlapping segmentation method that combined patch-level prediction with a multi-instance learning approach to integrate features across the slide images. Subjects were categorized into high- or low-risk groups based on the median risk score derived from the model, and the significance of this stratification was assessed using a log-rank test. In addition, combining transcriptomic data from patients and data from other large cohort studies, we further searched for genes associated with pathological features and their prognostic value. A total of 165 gastric cancer patients were included for model training, and a total of 26 features were integrated through multi-instance learning, with each process generating 11 probabilistic features and 2 predictive labeling features. We applied a 10-fold Lasso-Cox regression model to achieve dimensionality reduction of these features. The predictive accuracy of the model was verified using Kaplan-Meyer (KM) curves for stratification with a consistency index of 0.741 for the training set and 0.585 for the test set. Deep learning-based resultant supervised pathohistological features have the potential for superior prognostic stratification of gastric cancer patients, transforming image pixels into an effective and labor-saving tool to optimize the clinical management of gastric cancer patients. Also, SLITRK4 was identified as a prognostic marker for gastric cancer.

Identification of a novel prognostic signature for breast cancer derived from post-translational ubiquitin and ubiquitin-like modification-related genes.

Ubiquitin and ubiquitin-like (UUL) modifications play pleiotropic functions and are subject to fine regulatory mechanisms frequently altered in cancer. However, the comprehensive impact of UUL modification on breast cancer remains unclear. Transcriptomic and clinical data of breast cancer were downloaded from TCGA and GEO databases. Molecular subtyping of breast cancer was conducted using the NMF and CIBERSORT algorithms. Prognostic genes were identified via univariate, lasso and multivariate Cox regression analyses. Clinical pathological features, immune cell infiltration, immune therapeutic response and chemotherapy drug sensitivity were compared between groups using the Wilcoxon test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A total of 63 UUL modification-related genes were differentially expressed, with 29 up-regulated and 34 down-regulated genes. These genes were used to generate two UUL modification patterns that exhibited significant differences in prognostic features and immune cell infiltration. The UUL modification patterns were associated with 2038 differentially expressed genes that were significantly enriched in nuclear division, chromosome segregation, neuroactive ligand-receptor interaction, cell cycle, and other biological processes. Of these genes, 425 were associated with breast cancer prognosis, which enabled the classification of breast cancer into two clusters with significantly distinct prognoses. We developed a prognostic model, UULscore, which comprised nine genes and showed a significant correlation with partial immune cell infiltration. Furthermore, UULscore demonstrated potential predictive value in breast cancer overall survival prediction, immune therapeutic response, and chemotherapy drug sensitivity. UULscore, stage, radiotherapy, and chemotherapy were identified as independent prognostic factors for breast cancer. Based on these factors, a nomogram model was constructed, which demonstrated exceptional prognostic predictive performance. The present study identified two UUL modification-derived molecular subtypes in breast cancer, and have successfully constructed a risk-scoring model that holds potential value in prognosis, immune infiltration, immune therapeutic response, and chemotherapy sensitivity.

E2F1 Promotes the Occurrence of Head and Neck Squamous Cell Carcinoma and Serves as a Prognostic Biomarker.

Head and neck squamous cell carcinoma (HNSCC) is a common malignant tumor occurring in various sites such as the oral cavity, pharynx, larynx, and nasal cavity. This study aimed to explore the biological functions and prognostic value of E2F transcription factor 1 (E2F1) in HNSCC. Transcriptome and single-cell sequencing (scRNA-seq) data of HNSCC patients were analyzed using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). All samples were divided into high and low expression groups based on the expression levels of E2F1. A risk model was constructed based on Lasso-Cox regression, and the differences between the two groups in terms of prognosis were explored. The scRNA-seq data of HNSCC samples were analyzed using the Seurat package to identify cell types. AUCell was used to score different types of cells, and subsequently, the interaction pathways between the high-scoring cell population and other cell populations were explored using the CellChat package. The expression level of E2F1 in tumor tissues was higher than that in normal tissues, which was confirmed by in vitro experiments. Analysis of transcriptome data from TCGA revealed significant differences in overall survival (OS) between the high and low expression groups. Prognostic genes were selected based on DEGs between the two groups, and a risk model was constructed. Subsequently, a nomogram model was constructed based on clinical factors and risk scores, which exhibited good predictive performance. The expression landscape of prognostic genes in different cell types was explored using scRNA-seq data of HNSCC samples. Dendritic cell populations were identified as high-scoring cell populations, and the pathways of interaction between this cell population and other cell populations were explored. We identified E2F1 as an independent prognostic factor closely associated with the prognosis and immune response of HNSCC.

Investigating the diagnostic and prognostic significance of genes related to fatty acid metabolism in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide, with death rates increasing by approximately 2–3% per year. The high mortality and poor prognosis of HCC are primarily due to inaccurate early diagnosis and lack of monitoring when liver transplantation is not feasible. Fatty acid (FA) metabolism is a critical metabolic pathway that provides energy and signaling factors in cancer, particularly in HCC, and promotes malignancy. Therefore, it is essential to explore specific FA metabolism-related diagnostic and prognostic signatures that can enable the effective early diagnosis and monitoring of HCC.MethodsIn this study, we used genes associated with FA metabolism pathway and weighted gene co-expression network analysis (WGCNA) to establish a gene co-expression network and identify hub genes related to HCC (disease WGCNA) and FA clusters (cluster WGCNA). A diagnostic model was constructed using data downloaded from the Gene Expression Omnibus database (GSE25097), and a prognostic model was established using The Cancer Genome Atlas cohort, in which Univariate Cox regression analysis, multivariate Cox risk model, and LASSO Cox regression analysis were applied. The immune infiltration of HCC cells was evaluated using CIBERSORT. The function of the key SLC22A1 gene was experimentally verified in vitro and in vivo.ResultsTwelve overlapping genes (CPEB3, ASPDH, DEPDC7, ETFDH, UGT2B7, GYS2, F11, ANXA10, CYP2C8, GLYATL1, C6, and SLC22A1) from disease and cluster WGCNA were identified as key genes and used in the construction of the diagnostic and prognostic models. The RF model had the highest area under the ROC curve (AUC) of 0.994 was identified as the most effective for distinguishing patients with HCC with different features. The top five important genes (C6, UGT2B7, SLC22A1, F11, and CYP2C8) from the RF model were selected as diagnostic genes for further analysis (ROC curves: AUC value = 0.986, 95% confidence interval [95% CI] = 0.967–0.999). Moreover, a risk score formula consisting of four genes (GYS2, F11, ANXA10 and SLC22A1) was established and its independent prognostic ability was further demonstrated (univariate Cox regression analysis: hazard ratio [HR] = 3.664%, 95% CI = 2.033–6.605, P < 0.001; multivariate Cox regression analysis: HR = 2.801%, 95% CI = 1.553–5.049, P < 0.001). Additionally, in vitro and in vivo experiments demonstrated that SLC22A1 inhibits HCC tumor development, suggesting it may be a potential therapeutic target for HCC.ConclusionsThese findings indicate a considerable value of specific FA metabolism-related genes in the diagnostic and prognostic evaluation of HCC, which provide novel insights into the disease’s management, as well as has potential implications for personalized treatment strategies. However, further investigation of the effects of these model genes on HCC is required.

Early prediction of acute-on-chronic liver failure development in patients with diverse chronic liver diseases

Acute-on-chronic liver failure (ACLF) is a syndrome characterized by the acute decompensation of chronic liver disease, resulting in organ failure and high short-term mortality. The progression of ACLF is dynamic and reversible in a considerable proportion of patients during hospitalization. Early detection and accurate assessment of ACLF are essential; however, ideal methods for this purpose are still lacking. Therefore, this study aimed to develop a new score for predicting the onset of ACLF in patients with various chronic liver diseases.A total of 6,188 patients with various chronic liver diseases were included in the study. Clinical and laboratory data were collected, and the occurrence of ACLF within 28 days was recorded. The Lasso-Cox regression method was employed to develop prediction models for the onset of ACLF at 7, 14, and 28 days. Among 5,221 patients without ACLF, 477 progressed to ACLF within 28 days. Seven predictors were identified as significantly associated with the occurrence of ACLF at 7, 14, and 28 days. A new scoring system was developed as follows: [NEUT ≥ 7, 109/L; 1 or 0] × 0.49 + [PLT < 100, 109/L; 1 or 0] × 0.44 + [TBIL ≥ 35, µmol/L; 1 or 0] × 0.05 + [HDL-C < 0.5, mmol/L; 1 or 0] × 1.04 – Ln[Hb, g/L] × 0.89 + [BUN > 7, mmol/L; 1 or 0] × 0.51 + Ln[INR] × 0.87 + 3.40. This new score demonstrated superior discrimination, with the C-indexes of 0.958, 0.944, and 0.938 at 7, 14, and 28 days, respectively, outperforming those of four other scores (CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs score; all P < 0.001). Additionally, the new score improved in predictive accuracy, time-dependent receiver operating characteristics, probability density function evaluations, and calibration curves, making it highly predictive for the onset of ACLF at all time points. The optimal cut-off value of 9.6 effectively distinguished between high- and low-risk patients for ACLF onset. These findings were further validated in a separate cohort of patients. A new progressive score, based on seven predictors, has been developed to accurately forecast the occurrence of ACLF within 7, 14, and 28 days in patients with various chronic liver diseases. This tool may be utilized to identify high-risk patients, tailor follow-up management, and guide the escalation of care, prognostication, and transplant evaluation.

Lipid metabolism-related gene signature predicts prognosis and unveils novel anti-tumor drugs in specific type of diffuse large B cell lymphoma

BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma which possess highly aggressive and heterogeneous. Despite advances in understanding heterogeneity and development of novel targeted agents, the prognosis of DLBCL patients remains unsatisfied. Lipids are crucial components of biological membranes and signal transduction while accumulating evidence has supported the vital roles of abnormal lipid metabolism in tumorigenesis. Furthermore, some related pathways could serve as prognostic biomarkers and potential therapeutic targets. However, the clinical significance of abnormal lipid metabolism reprogramming in DLBCL has not been investigated. In the current study, we developed a prognostic risk model for DLBCL based on the abnormal expressed lipid metabolism genes and moreover based on our risk model we classified patients with DLBCL into novel subtypes and identified potential drugs for DLBCL patients with certain lipid metabolism profiles.MethodsWe utilized univariate Cox regression analysis to identify the prognosis-related lipid metabolism genes, and then performed LASSO Cox regression to identify prognostic related lipid metabolism related genes. Multivariate cox regression was used to establish the prognostic model. Patients were divided in to high and low risk groups based on the median risk score. Immune cell infiltration and GSEA were used to identify the pathways between high and low risk groups. Oncopredict algorithm was utilized to identify potential drug for high-risk patients. In vitro cell apoptosis and viability analysis were employed to verify the specific tumor inhibition effects of AZD5153.ResultsNineteen survival related lipid metabolism genes TMEM176B, LAYN, RAB6B, MMP9, ATAD3B, SLC2A11, CD3E, SLIT2, SLC2A13, SLC43A3, CD6, SIRPG, NEK6, LCP2, CTTN, CXCL2, SNX22, BCL6 and FABP4 were identified and subjected to build the prognostic model which was further verified in four external microarray cohorts and one RNA seq cohorts. Tumor immune microenvironment analysis and GSEA results showed that the activation of MYC targets genes rather than immunosuppression contribute to the poor survival outcome of patients in the high-risk group. AZD5153, a novel bivalent BET bromodomain inhibitor which could inhibit the transcription of MYC and E2F exhibited specific antitumor function for cells with high-risk score.ConclusionsOur results provide the first lipid metabolism-based gene signature for predicting the survival of patients with DLBCL. Furthermore, by determining novel subtypes with our lipid metabolism prognostic model we illustrated that drugs that compromising MYC target genes rather than immune checkpoint inhibitors may be beneficial to DLBCL patients with certain lipid metabolism profiles.