Subtype C1 Research Articles

Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma

BackgroundUterine Corpus Endometrial Carcinoma (UCEC) is a highly heterogeneous tumor, and limitations in current diagnostic methods, along with treatment resistance in some patients, pose significant challenges for managing UCEC. The excessive activation of G2/M checkpoint genes is a crucial factor affecting malignancy prognosis and promoting treatment resistance.MethodsGene expression profiles and clinical feature data mainly came from the TCGA-UCEC cohort. Unsupervised clustering was performed to construct G2/M checkpoint (G2MC) subtypes. The differences in biological and clinical features of different subtypes were compared through survival analysis, clinical characteristics, immune infiltration, tumor mutation burden, and drug sensitivity analysis. Ultimately, an artificial neural network (ANN) and machine learning were employed to develop the G2MC subtypes classifier.ResultsWe constructed a classifier based on the overall activity of the G2/M checkpoint signaling pathway to identify patients with different risks and treatment responses, and attempted to explore potential therapeutic targets. The results showed that two G2MC subtypes have completely different G2/M checkpoint-related gene expression profiles. Compared with the subtype C2, the subtype C1 exhibited higher G2MC scores and was associated with faster disease progression, higher clinical staging, poorer pathological types, and lower therapy responsiveness of cisplatin, radiotherapy and immunotherapy. Experiments targeting the feature gene KIF23 revealed its crucial role in reducing HEC-1A sensitivity to cisplatin and radiotherapy.ConclusionIn summary, our study developed a classifier for identifying G2MC subtypes, and this finding holds promise for advancing precision treatment strategies for UCEC.

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.

A disulfidptosis-associated long noncoding RNA signature to predict low-grade glioma classification, prognosis, tumor microenvironment, and therapy regimens: Observational study.

This study aimed to investigate the function of disulfidptosis-associated long noncoding RNAs (DAlncRNAs) in low-grade gliomas (LGG) through bioinformatics analysis and construct a signature to predict the classification, prognosis, tumor microenvironment, and selection of immunotherapy and chemotherapy in LGG. Genomic, clinical, and mutational information of 526 patients with LGG was retrieved from The Cancer Genome Atlas repository. A nonnegative matrix factorization algorithm was applied to classify patients with LGG. Univariate, LASSO regression, and multivariate Cox regression analyses were performed to determine prognostic DAlncRNAs. Following the median risk score, we defined the sample as a high-risk (HR) or low-risk group. Finally, survival, receiver operating characteristic curve, risk curve, principal component, independent prognosis, risk difference, functional enrichment, tumor microenvironment, immune cell infiltration, mutation, and drug sensitivity analyses were performed. Patients were classified into C1 and C2 subtypes associated with disulfidptosis. Eight prognostic DAlncRNAs (AC003035.2, AC010157.2, AC010273.3, AC011444.3, AC092667.1, AL450270.1, AL645608.2, and LINC01571) were identified, and a prognostic signature of LGG was developed. The DAlncRNA-based signature was found to be an independent prognostic factor in patients with LGG, thereby constructing a nomogram. In addition, in the HR group, immune function was more active and the tumor mutation burden was higher. The patients were mainly composed of subtype C2, and their prognosis was worse. Immunotherapy and chemotherapy were predicted in the HR and low-risk groups, respectively. Our study, based on DAlncRNAs, highlights 2 disulfidptosis-associated LGG subtypes with different prognostic and immune characteristics and creates a novel disulfidptosis-associated prognostic signature, which may inform the classification, prognosis, molecular pathogenesis, and therapeutic strategies for patients with LGG.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1,987 ovarian cancer patients spanning 26 independent GEO cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust 10-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Driver mutation subtypes involve with differentiated immunophenotypes influencing pancreatic cancer outcomes

Despite many studies focusing on the prognostic biomarkers in pancreatic adenocarcinomas (PAADs), there is ill-informed about the relationships between their genomic features and immune characteristics. Herein, we deeply investigated the involvement of major driver mutation subtypes with immunophenotypes impacting PAAD outcomes. Based on public data analyses of RNA expression-based immune subtypes in PAAD, in contrast to KRAS G12D &TP53 co-mutant patients with poor outcomes, the best immune subtype C3 (inflammatory) characterized by high Th1/Th2 ratio was relatively enriched in KRASnon−G12DTP53wt patients with better survival, whereas the inferior subtype C2 (IFN-γ dominant) with low Th1/Th2 ratio was more common in the former than in the latter. Moreover, contrary to the highly immunosuppressive microenvironment (high Treg, high ratio of Treg to tumor-specific CD4+ T cell) in KRASG12DTP53mut patients, KRASG12VTP53wt individuals exhibited an inflamed context profiled by multiplex immunohistochemistry. It could be responsible for their outstanding survival advantage over others in postsurgical PAAD patients receiving adjuvant chemotherapy as shown by our cohort. Together, KRASG12VTP53wt may be a promising biomarker for prognostic evaluation and screening certain candidates with PAAD to get desirable survival benefit from adjuvant chemotherapy.

Gene signature developed based on programmed cell death to predict the therapeutic response and prognosis for liver hepatocellular carcinoma

BackgroundThe prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC. MethodsMolecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed. ResultsPCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1–C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil. ConclusionA PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.

Stem cell index-based RiskScore model for predicting prognosis in thyroid cancer and experimental verification

ObjectiveAn mRNA expression-based stemness index (mRNAsi) has been developed to characterize cancer stemness. However, the predictive value of mRNAsi-based signature in therapeutic resistance and immunotherapy in thyroid cancer (THCA) remains unclarified. This study evaluated and validated the role of mRNAsi in drug sensitivity, its relationship between mRNAsi and THCA clinical features and immunity based on bioinformatics. MethodsBased on transcriptome data of THCA patients from the Tumor Genome Atlas Project (TCGA) database, and expression data of multifunctional stem cell samples from the Progenitor Cell Biology Consortium (PCBC) databases, mRNAsi was calculated by the " one class logistic regression (OCLR)" method, Molecular subtypes of TCGA-THCA samples were identified with mRNAsi-related genes using ConsensusClusterPlus method. The gene mutation, clinical characteristics, immune characteristics, TIDE and drug sensitivity were compared among molecular subtypes. A prognostic model was designed with Lasso cox method. Modulation of malignant phenotype of THCA cell lines by model characterization genes is validated by CCK-8, flow cytometry. DNA methylation disorder in promoter region was analyzed between risk groups. The model was validated for survival in the internal Test dataset, while TCGA pan-cancer and immunotherapy datasets were further employed to validate the performance of this model. ResultsWe obtained a total of 78 stem cell samples, each containing the expression profile of 8087 mRNA genes. Based on mRNAsi, THCA was divided into 3 subtypes. Subtype C2 had the poorest prognosis and highest immune score, while subtype C3 had the best prognosis, lowest mRANsi and highest TIDE score. Patients in subtype C2 showed higher sensitivity to Cisplatin, Erlotinib, Paclitaxel, and Lapatinib. The prognostic signature was generated using 5 mRNAsi-related genes, which could predict prognosis for THCA. qRT-PCR results showed that the expression of 5 genes were various in Hth7 and KTC-1 cells, and inhibition CELSR3 expression increased percentage of apoptosis in Hth7 and KTC-1 cells. mRNAsi related DNA methylation sites were mainly enriched in tumor related pathways. Good performance of this model was validated in Test dataset, pan-cancer and immunotherapy datasets. ConclusionThis study identified three subtypes for classification and developed a prognostic model with mRNAsi-related genes, which provided great potential for prognosis and immunotherapy prediction.

Defining three ferroptosis-based molecular subtypes and developing a prognostic risk model for high-grade serous ovarian cancer.

As a newly defined regulated cell death, ferroptosis is a potential biomarker in ovarian cancer (OV). However, its underlying mechanism in tumor microenvironment (TME) and clinical prediction significance in OV remained to be elucidated. The transcriptome data of high-grade serous OV from The Cancer Genome Atlas (TCGA) database were downloaded. Molecular subtypes were classified based on ferroptosis-correlated genes from the FerrDb database by performing consensus clustering analysis. The associations between the subtypes and clinicopathologic characteristics, mutation, regulatory pathways and immune landscape were assessed. A ferroptosis-related prognostic model was constructed and verified using International Cancer Genome Consortium (ICGC) cohort and GSE70769. Three molecular subtypes of OV were defined. Patients in subtype C3 tended to have the most favorable prognosis, while subtype C1 showing more mesenchymal cells, increased immune infiltration of Macrophages_M2, lower tumor purity, and epithelial-to-mesenchymal transition (EMT) features had the poorest prognosis. A ferroptosis-related risk model was constructed using 8 genes (PDP1, FCGBP, EPHA4, GAS1, SLC7A11, BLOC1S1, SPOCK2, and CXCL9) and manifested a strong prediction performance. High-risk patients had enriched EMT pathways, more Macrophages_M2, less plasma cells and CD8 cell infiltration, greater tendency of immune escape and worse prognosis. The risk score has negatively correlated relation with LAG3, TIGIT, CTLA4, IDO1, CD27, ICOS, and IL2RB but positively correlated with PVR, CD276, and CD28. Moreover, low-risk patients were more sensitive to Cisplatin and Gefitinib, Gemcitabine. Our results could improve the understanding of ferroptosis in OV, providing promising insights for the clinical targeted therapy for the cancer.

Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics

ObjectiveThis study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics. MethodsUsing a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the “CancerSubtypes” package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics. ResultsClustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm. ConclusionUnsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma.

Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients. Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA). HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients' prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells. This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma.

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC. Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets. LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses. The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers.MethodsWe analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package.ResultsBatch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633–0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420–2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17–2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness.ConclusionThe DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

A 15-Inflammation-Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma

Chronic inflammation promotes the development of pancreatic ductal adenocarcinoma (PDAC) and PDAC-related inflammatory tumor microenvironment facilitates tumor growth and metastasis. Thus, we aimed to study the association between inflammatory response and prognosis in patients with PDAC. We conducted the whole transcriptomic sequencing using tissue samples collected from patients diagnosed with PDAC (n = 106) recruited from Shandong Cancer Hospital. We first constructed a prognostic signature using 15 inflammation-related genes in The Cancer Genome Atlas (TCGA) cohort (n = 177) and further validated it in an independent International Cancer Genome Consortium (ICGC) cohort (n = 90) and our in-house cohort. PDAC patients with a higher risk score had poorer overall survival (OS) (P < 0.001; HR, 3.02; 95% CI, 1.94-4.70). The association between the prognostic signature and OS remained significant in the multivariable Cox regression adjusting for age, sex, alcohol exposure, diabetes, and stage (P < 0.001; HR, 2.91; 95% CI, 1.73-4.89). This gene signature also robustly predicted prognosis in the ICGC cohort (P = 0.01; HR, 1.94; 95% CI, 1.14-3.30) and our cohort (P < 0.001; HR, 2.40; 95% CI, 1.45-3.97). Immune subtype C3 (inflammatory) was enriched and CD8+ T cells were higher in patients with a lower risk score (P < 0.05). Furthermore, PDAC patients with higher risk scores were more sensitive to chemotherapy, immunotherapy, and PARP inhibitors (P < 0.05). In sum, we identified a novel gene signature that was associated with inflammatory response for risk stratification, prognosis prediction, and therapy guidance in PDAC patients. Future studies are warranted to validate the clinical utility of the signature.

A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma

BackgroundProgrammed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.MethodsConsensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.ResultsFour molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.ConclusionThis study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.

Establishment of the molecular subtypes and a risk model for stomach adenocarcinoma based on genes related to reactive oxygen species

BackgroundOxidative stress promotes the development of stomach adenocarcinoma (STAD) and resistance of STAD patients to chemotherapy. This study developed a risk classification and prognostic model for STAD based on genes related to oxidative stress. MethodsUnivariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression analysis were performed using transcriptome data of STAD from The Cancer Genome Atlas (TCGA) and reactive oxygen species (ROS)-related genes from Gene Set Enrichment Analysis (GSEA) website to develop a risk model. Genetic landscape, pathway characteristics and immune characteristics between the two risk groups were assessed to evaluate patients’ response to anti-tumor therapy. Further, a nomogram was created to evaluate the clinical outcomes of STAD patients. The mRNA levels of genes were detected by reverse transcription quantitative PCR (RT-qPCR). ResultsTwo ROS-related molecular subtypes (subtype C1 and C2) were classified, with subtype C2 having unfavorable prognosis, higher immune score, and greater infiltration of macrophages, myeloid-derived suppressor cells (MDSCs), mast cells, regulatory T cells, and C–C chemokine receptor (CCR). Five ROS-related genes (ASCL2, COMP, NOX1, PEG10, and VPREB3) were screened to develop a prognostic model, the robustness of which was validated in TCGA and external cohorts. RT-qPCR analysis showed that ASCL2, COMP, NOX1, and PEG10 were upregulated, while the mRNA level of VPREB3 was downregulated in gastric cancer cells. The risk score showed a negative relation to tumor mutation burden (TMB). Low-risk patients exhibited higher mutation frequencies of TTN, SYNE1, and ARID1A, higher response rate to immunotherapy and were more sensitive to 32 traditional chemotherapeutic drugs, while high-risk patients were sensitive to 13 drugs. Calibration curve and DCA confirmed the accuracy and reliability of the nomogram. ConclusionThese findings provided novel understanding on the mechanism of ROS in STAD. The current study developed a ROS-related signature to help predict the prognosis of patients suffering from STAD and to guide personalized treatment.

HdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Integrated Transcriptome Analysis Reveals Molecular Subtypes and ceRNA Networks in Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS). While extensively studied, its molecular subtypes and mechanisms remain poorly understood, hindering the identification of effective therapeutic targets. We used ConsensusClusterPlus to analyze transcriptome data from 215 MS patient samples, identifying distinct molecular subtypes. Differential expression analysis and variability assessments were conducted to further characterize these subtypes. Additionally, circular RNAs (circRNAs) and microRNAs (miRNAs) were screened for potential ceRNA interactions. Three molecular subtypes were identified: MS-FCRL1 (C1), MS-BTG1 (C2), and MS-RPL38 (C3). Each subtype was involved in key MS-related pathways (as annotated by KEGG), but the core genes regulating these pathways differed significantly among the subtypes. Subtype C3 exhibited neurodegenerative pathway enrichment, increased immune activity, and immune cell infiltration, suggesting a more severe disease course. Further analysis revealed 18 differentially expressed circRNAs and 22 miRNAs, with EEF1D and TUBA1A as hub targets in C3. Differential activation of immune pathways across MS subtypes suggests specific gene expression drives disease heterogeneity. We propose a circ_0045537/miR-196a-5p/TUBA1A axis in subtype C3, modulating microtubule dynamics and worsening MS severity.

Three novel marine species of the genus Reichenbachiella exhibiting degradation of complex polysaccharides.

Three novel strains designated ABR2-5T, BKB1-1T, and WSW4-B4T belonging to the genus Reichenbachiella of the phylum Bacteroidota were isolated from algae and mud samples collected in the West Sea, Korea. All three strains were enriched for genes encoding up to 216 carbohydrate-active enzymes (CAZymes), which participate in the degradation of agar, alginate, carrageenan, laminarin, and starch. The 16S rRNA sequence similarities among the three novel isolates were 94.0%-94.7%, and against all three existing species in the genus Reichenbachiella they were 93.6%-97.2%. The genome sizes of the strains ABR2-5T, BKB1-1T, and WSW4-B4T were 5.5, 4.4, and 5.0 Mb, respectively, and the GC content ranged from 41.1%-42.0%. The average nucleotide identity and the digital DNA-DNA hybridization values of each novel strain within the isolates and all existing species in the genus Reichenbachiella were in a range of 69.2%-75.5% and 17.7-18.9%, respectively, supporting the creation of three new species. The three novel strains exhibited a distinctive fatty acid profile characterized by elevated levels of iso-C15:0 (37.7%-47.4%) and C16:1 ω5c (14.4%-22.9%). Specifically, strain ABR2-5T displayed an additional higher proportion of C16:0 (13.0%). The polar lipids were phosphatidylethanolamine, unidentified lipids, aminolipids, and glycolipids. Menaquinone-7 was identified as the respiratory quinone of the isolates. A comparative genome analysis was performed using the KEGG, RAST, antiSMASH, CRISPRCasFinder, dbCAN, and dbCAN-PUL servers and CRISPRcasIdentifier software. The results revealed that the isolates harbored many key genes involved in central metabolism for the synthesis of essential amino acids and vitamins, hydrolytic enzymes, carotenoid pigments, and antimicrobial compounds. The KEGG analysis showed that the three isolates possessed a complete pathway of dissimilatory nitrate reduction to ammonium (DNRA), which is involved in the conservation of bioavailable nitrogen within the ecosystem. Moreover, all the strains possessed genes that participated in the metabolism of heavy metals, including arsenic, copper, cobalt, ferrous, and manganese. All three isolated strains contain the class 2 type II subtype C1 CRISPR-Cas system in their genomes. The distinguished phenotypic, chemotaxonomic, and genomic characteristics led us to propose that the three strains represent three novel species in the genus Reichenbachiella: R. ulvae sp. nov. (ABR2-5T = KCTC 82990T = JCM 35839T), R. agarivorans sp. nov. (BKB1-1T = KCTC 82964T = JCM 35840T), and R. carrageenanivorans sp. nov. (WSW4-B4T = KCTC 82706T = JCM 35841T).

Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes.

As a complex group of malignancies, head and neck squamous cell carcinoma (HNSC) is one of the leading causes of cancer mortality. This study aims to establish a reliable clinical classification and gene signature for HNSC prognostic prediction and precision treatments. A consensus clustering analysis was performed to group HNSC patients in The Cancer Genome Atlas (TCGA) database based on genes linked to programmed cell death (PCD). Differentially expressed genes (DEGs) between subtypes were identified using the "limma" R package. The TCGA prognostic signature and PCD-related prognostic genes were found using a least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis. The robustness of the LASSO analysis was validated using datasets GSE65858 and GSE41613. A cell counting kit-8 (CCK-8) test, Western blot, and real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were used to evaluate the expression and viability of prognostic genes. Four molecular subtypes were identified in PCD-related genes. Subtype C4 had the best prognosis and the highest immune score, while subtype C1 exhibited the most unfavorable outcomes. Three hundred shared DEGs were identified among the four subtypes, and four prognostic genes (CTLA4, CAMK2N1, PLAU and CALML5) were used to construct a TCGA-HNSC prognostic model. High-risk patients manifested poorer prognosis, more inflammatory pathway enrichment, and lower immune cell infiltration. High-risk patients were more prone to immune escape and were more likely to be resistant to Cisplatin and 5-Fluorouracil. Prognosis prediction was validated in external datasets. The expression of CTLA4, CAMK2N1, PLAU and CALML5 was enhanced in CAL-27 and SCC-25 cell lines, and CALML5 inhibited CAL-27 and SCC-25 cell viability. This study shares novel insights into HNSC classification and provides a reliable PCD-related prognostic signature for prognosis prediction and treatment for patients with HNSC.