Multi‐omics consensus ensemble refines the classification of muscle‐invasive bladder cancer with stratified prognosis, tumour microenvironment and distinct sensitivity to frontline therapies

Deciphering the molecular heterogeneity of intermediate- and (very-)high-risk non-muscle-invasive bladder cancer using multi-layered -omics studies

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered -omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different -omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner.

Novel tumor gene expression signatures improve the overall survival prediction efficiency over tumor mutation burden and PD-L1 expression in bladder carcinoma with checkpoint blockade immunotherapy

Although immune checkpoint blockade therapy (ICBT) has revolutionized cancer treatment with good therapeutic response in a number of human cancers, including bladder cancer, many cancers still do not respond to ICBT. Analyzing genetic signatures helps the understanding of underlying biological mechanisms. Here, based on two cohorts of bladder cancer patients receiving ICBT, we identified three novel ICBT-associated signatures in the bladder cancer microenvironment, involving genomic stability, angiogenesis and RNA regulatory, which affect PD-L1 expression and patient response to ICBT. The combinations of these signatures with TMB or PD-L1 expression improved the overall survival prediction efficiency over TMB and PD-L1 expression alone for patients receiving ICBT. Moreover, we utilized two methods to search potential drugs or small-molecules that have an impact on ICBT-associated signatures. This study provides new molecular insight into ICBT response of bladder cancer and has the potential to improve the prediction accuracy for patients to benefit from ICBT.

A metabolic reprogramming-related gene signature correlates with prognosis and proliferation of BLCA

Bladder cancer (BLCA) is one of the most frequent urothelium carcinoma, but with poor prognosis due to lack of reliable predictive biomarkers. Metabolic reprogramming involving in various nutrients, and is reported to be closely associated with malignant progression in BLCA. With the use of transcriptome sequencing data profiles of 349 patients from The Cancer Genome Atlas, we established a three-gene glycolysis-related signature to predict the prognosis of BLCA patients. Our signature constructed on the basis of AK3, GALK1 and NUP205 expression, detail features and interactions between these three genes were further explored. We established a nomogram by integrating clinical variables and the risk score. Glycolytic level and proliferation ability were detected to study the role and mechanisms of NUP205 on BLCA. The connections between three genes in our signature were independent. We found our signature gains more value for patients with highly malignant stage. The established nomogram also confirmed that the signature had a eligible clinically predict capacity. After inhibited NUP205 expression, we found the glycolysis level of BLCA cells decreased and proliferation ability suppressed, mainly through AMPK signaling pathway inactivation. Collectively, our study explored a three-gene glycolysis-related signature that predict the prognosis of patients with BLCA, and highlights NUP205 as a potential therapeutic target for inhibiting glycolytic processes and proliferation in BLCA cells.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

ecGBMsub: an integrative stacking ensemble model framework based on eccDNA molecular profiling for improving IDH wild-type glioblastoma molecular subtype classification

IDH wild-type glioblastoma (GBM) intrinsic subtypes have been linked to different molecular landscapes and outcomes. Accurate prediction of molecular subtypes of GBM is very important to guide clinical diagnosis and treatment. Leveraging machine learning technology to improve the subtype classification was considered a robust strategy. Several single machine learning models have been developed to predict survival or stratify patients. An ensemble learning strategy combines several basic learners to boost model performance. However, it still lacked a robust stacking ensemble learning model with high accuracy in clinical practice. Here, we developed a novel integrative stacking ensemble model framework (ecGBMsub) for improving IDH wild-type GBM molecular subtype classification. In the framework, nine single models with the best hyperparameters were fitted based on extrachromosomal circular DNA (eccDNA) molecular profiling. Then, the top five optimal single models were selected as base models. By randomly combining the five optimal base models, 26 different combinations were finally generated. Nine different meta-models with the best hyperparameters were fitted based on the prediction results of 26 different combinations, resulting in 234 different stacked ensemble models. All models in ecGBMsub were comprehensively evaluated and compared. Finally, the stacking ensemble model named "XGBoost.Enet-stacking-Enet" was chosen as the optimal model in the ecGBMsub framework. A user-friendly web tool was developed to facilitate accessibility to the XGBoost.Enet-stacking-Enet models (https://lizesheng20190820.shinyapps.io/ecGBMsub/).

Development of gene panel for predicting recurrence in early-stage cervical cancer patients

Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

PURPOSE

The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.

METHODS

We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.

RESULTS

Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.

CONCLUSIONS

MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

DNA hypomethylation patterns and their impact on the tumor microenvironment in colorectal cancer

BACKGROUND

Recent research underscores the pivotal role of immune checkpoints as biomarkers in colorectal cancer (CRC) therapy, highlighting the dynamics of resistance and response to immune checkpoint inhibitors. The impact of epigenetic alterations in CRC, particularly in relation to immune therapy resistance, is not fully understood.

METHODS

We integrated a comprehensive dataset encompassing TCGA-COAD, TCGA-READ, and multiple GEO series (GSE14333, GSE37892, GSE41258), along with key epigenetic datasets (TCGA-COAD, TCGA-READ, GSE77718). Hierarchical clustering, based on Euclidean distance and Ward's method, was applied to 330 primary tumor samples to identify distinct clusters. The immune microenvironment was assessed using MCPcounter. Machine learning algorithms were employed to predict DNA methylation patterns and their functional enrichment, in addition to transcriptome expression analysis. Genomic mutation profiles and treatment response assessments were also conducted.

RESULTS

Our analysis delineated a specific tumor cluster with CpG Island (CGI) methylation, termed the Demethylated Phenotype (DMP). DMP was associated with metabolic pathways such as oxidative phosphorylation, implicating increased ATP production efficiency in mitochondria, which contributes to tumor aggressiveness. Furthermore, DMP showed activation of the Myc target pathway, known for tumor immune suppression, and exhibited downregulation in key immune-related pathways, suggesting a tumor microenvironment characterized by diminished immunity and increased fibroblast infiltration. Six potential therapeutic agents-lapatinib, RDEA119, WH.4.023, MG.132, PD.0325901, and AZ628-were identified as effective for the DMP subtype.

CONCLUSION

This study unveils a novel epigenetic phenotype in CRC linked to resistance against immune checkpoint inhibitors, presenting a significant step toward personalized medicine by suggesting epigenetic classifications as a means to identify ideal candidates for immunotherapy in CRC. Our findings also highlight potential therapeutic agents for the DMP subtype, offering new avenues for tailored CRC treatment strategies.

Multi-omics comprehensive analyses of programmed cell death patterns to regulate the immune characteristics of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with high morbidity and mortality. Triggering the programmed cell death (PCD) to enhance the anti-tumor therapies is being applied in multiple cancers. However, the limited understanding of genetic heterogeneity in HNSCC severely hampers the clinical efficacy. We systematically analyzed 14 types of PCD in HNSCC from The Cancer Genome Atlas (TCGA). We utilized ssGSEA to calculate the PCD scores and classify patients into two clusters. Subsequently, we displayed the genomic alteration landscape to unravel the significant differences in copy number alterations and gene mutations. Furthermore, we calculated the IC50 values of targeted drugs to predict the differences in sensitivity. To identify the immune-related prognostic types, we comprehensively estimated the relationship between immune indicators and all prognostic PCD in three datasets (TCGA, GSE65858, GSE41613). Finally, 7 regulators were filtered. Subsequently, we integrated 10 machine learning algorithms and 101 algorithm combinations to test the clinical predictive efficacy. Using WGCNA as a basis, we built a weighted co-expression network to identify modules involved in the immune landscape with different colors. Meanwhile, our results indicated that blue and red modules containing crucial regulators closely related to the CD4+, CD8+ T cells, TMB or PD-L1. FCGR2A from blue module, CSF2, INHBA, and THBS1 from the red module were determined. After verifying in vivo experiments, FCGR2A was identified as hub gene. In conclusion, our findings suggest a potential role of PCD in HNSCC, offering new insights into effective immunotherapy and anti-tumor therapies in HNSCC.

Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer

Ovarian cancer (OC) is a highly heterogeneous disease, with patients at different tumor staging having different survival times. Metabolic reprogramming is one of the key hallmarks of cancer; however, the significance of metabolism-related genes in the prognosis and therapy outcomes of OC is unclear. In this study, we used weighted gene coexpression network analysis and differential expression analysis to screen for metabolism-related genes associated with tumor staging. We constructed the metabolism-related gene prognostic index (MRGPI), which demonstrated a stable prognostic value across multiple clinical trial end points and multiple validation cohorts. The MRGPI population had its distinct molecular features, mutational characteristics, and immune phenotypes. In addition, we investigated the response to immunotherapy in MRGPI subgroups and found that patients with low MRGPI were prone to benefit from anti-PD-1 checkpoint blockade therapy and exhibited a delayed treatment effect. Meanwhile, we identified four candidate therapeutic drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients with high MRGPI, and we evaluated the pharmacokinetics and safety of the candidate drugs. In summary, the MRGPI was a robust clinical feature that could predict patient prognosis, immunotherapy response, and candidate drugs, facilitating clinical decision making and therapeutic strategy of OC.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BACKGROUND

Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC).

METHODS

HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types.

RESULTS

With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients.

CONCLUSION

We built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

Pathogenomics model for personalized medicine in cervical cancer: Cross-talk of gene expressions and pathological images related to oxidative stress

An increasing number of studies have shown that oxidative stress plays an important role in the development and progression of cancer. Cervical cancer (CC) is a disease of unique complexity that tends to exhibit high heterogeneity in molecular phenotypes. We aim here to characterize molecular features of cervical cancer by developing a classification system based on oxidative stress-related gene expression profiles. In this study, we obtained gene expression profiling data for cervical cancer from the TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) (GSE44001) databases. Oxidative stress-related genes used for clustering were obtained from GeneCards. Patients with cervical cancer were divided into two subtypes (C1 and C2) by non-negative matrix factorization (NMF) classification. By performing Kaplan-Meier survival analysis, differential expression analysis, and gene set enrichment analysis (GSEA) between the two subtypes, we found that subtype C2 had a worse prognosis and was highly enriched for immune-related pathways as well as epithelial-mesenchymal transition (EMT) pathways. Subsequently, we performed metabolic pathway analysis, gene mutation landscape analysis, immune microenvironment analysis, immunotherapy response analysis, and drug sensitivity analysis of the two isoforms. The results showed that the isoforms were significantly different between metabolic pathway enrichment and the immune microenvironment, and the chromosomes of subtype C1 were more unstable. In addition, we found that subtype C2 tends to respond to treatment with anti-CTLA4 agents, a conclusion that coincides with high chromosomal variation in C1, as well as C2 enrichment of immune-related pathways. Then, we screened 10 agents that were significantly susceptible to C2 subtype. Finally, we constructed pathogenomics models based on pathological features and linked them to molecular subtypes. This study establishes a novel CC classification based on gene expression profiles of oxidative stress-related genes and elucidates differences between immune microenvironments between CC subtypes, contributing to subtype-specific immunotherapy and drug therapy.

Identification of an inflammation-related risk signature for prognosis and immunotherapeutic response prediction in bladder cancer

Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.

Delineating the interplay between oncogenic pathways and immunity in anaplastic Wilms tumors

Wilms tumors are highly curable in up to 90% of cases with a combination of surgery and radio-chemotherapy, but treatment-resistant types such as diffuse anaplastic Wilms tumors pose significant therapeutic challenges. Our multi-omics profiling unveils a distinct desert-like diffuse anaplastic Wilms tumor subtype marked by immune/stromal cell depletion, TP53 alterations, and cGAS-STING pathway downregulation, accounting for one-third of all diffuse anaplastic cases. This subtype, also characterized by reduced CD8 and CD3 infiltration and active oncogenic pathways involving histone deacetylase and DNA repair, correlates with poor clinical outcomes. These oncogenic pathways are found to be conserved in anaplastic Wilms tumor cell models. We identify histone deacetylase and/or WEE1 inhibitors as potential therapeutic vulnerabilities in these tumors, which might also restore tumor immunogenicity and potentially enhance the effects of immunotherapy. These insights offer a foundation for predicting outcomes and personalizing treatment strategies for aggressive pediatric Wilms tumors, tailored to individual immunological landscapes.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

A three-stage eccDNA based molecular profiling significantly improves the identification, prognosis assessment and recurrence prediction accuracy in patients with glioma

Glioblastoma (GBM) progression is influenced by intratumoral heterogeneity. Emerging evidence has emphasized the pivotal role of extrachromosomal circular DNA (eccDNA) in accelerating tumor heterogeneity, particularly in GBM. However, the eccDNA landscape of GBM has not yet been elucidated. In this study, we first identified the eccDNA profiles in GBM and adjacent tissues using circle- and RNA-sequencing data from the same samples. A three-stage model was established based on eccDNA-carried genes that exhibited consistent upregulation and downregulation trends at the mRNA level. Combinations of machine learning algorithms and stacked ensemble models were used to improve the performance and robustness of the three-stage model. In stage 1, a total of 113 combinations of machine learning algorithms were constructed and validated in multiple external cohorts to accurately distinguish between low-grade glioma (LGG) and GBM in patients with glioma. The model with the highest area under the curve (AUC) across all cohorts was selected for interpretability analysis. In stage 2, a total of 101 combinations of machine learning algorithms were established and validated for prognostic prediction in patients with glioma. This prognostic model performed well in multiple glioma cohorts. Recurrent GBM is invariably associated with aggressive and refractory disease. Therefore, accurate prediction of recurrence risk is crucial for developing individualized treatment strategies, monitoring patient status, and improving clinical management. In stage 3, a large-scale GBM cohort (including primary and recurrent GBM samples) was used to fit the GBM recurrence prediction model. Multiple machine learning and stacked ensemble models were fitted to select the model with the best performance. Finally, a web tool was developed to facilitate the clinical application of the three-stage model.

Preoperative fluorescence in situ hybridization analysis as a predictor of tumor recurrence in patients with non-muscle invasive bladder cancer: a bi-institutional study

BACKGROUND

Non-muscle invasive bladder cancer (NMIBC) is known for its elevated recurrence rate, necessitating an enhancement in the current risk stratification for recurrence. The urine-based fluorescence in situ hybridization (FISH) assay has emerged as a noninvasive auxiliary tool for detecting bladder cancer. The aim of this study was to explore the potential relationship between the preoperative FISH assay and recurrence, and to develop a FISH-clinical nomogram for predicting the recurrence-free survival (RFS) in NMIBC patients.

METHODS

In total, 332 eligible patients were enrolled from two hospitals. The SYSMH cohort was randomly assigned to the training set (n = 168) and the validation set I (n = 72) at a ratio of 7:3, while the SYSUTH cohort was allocated to the validation set II (n = 92). The correlation between the preoperative FISH assay and recurrence was determined through the Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used for model construction. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness.

RESULTS

We uncovered that chromosome 7 aneuploidy, p16 locus loss, number of the positive FISH sites, and the FISH test result were significantly associated with tumor recurrence. Then, a FISH-clinical nomogram incorporating the FISH test result, T stage, associated CIS, tumor grade, and tumor status was developed. It showed favorable calibration and discrimination with a C-index of 0.683 (95%CI, 0.611-0.756) in the training set, which was confirmed in the validation set I and validation set II with C-indexes of 0.665 (95%CI, 0.565-0.765) and 0.778 (95%CI, 0.665-0.891), respectively. Decision curve analysis revealed the clinical usefulness of the nomogram. Moreover, our proposed nomogram significantly outperformed the guideline-recommended EORTC and CUETO scoring models.

CONCLUSION

Our study confirmed the prognostic value of the preoperative FISH assay and proposed a FISH-clinical nomogram to predict RFS in NMIBC patients. Our nomogram can serve as a more precise tool for recurrence risk stratification, which may optimize disease management in bladder cancer and improve patient prognosis.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer

Muscle-invasive urothelial cancer (MUC), characterized by high aggressiveness and significant heterogeneity, is currently lacking highly precise individualized treatment options. We used a computational pipeline to synthesize multiomics data from MUC patients using 10 clustering algorithms, which were then combined with 10 machine learning algorithms to identify molecular subgroups of high resolution and develop a robust consensus machine learning-driven signature (CMLS). Through multiomics clustering, we identified three cancer subtypes (CSs) that are related to prognosis, with CS2 exhibiting the most favorable prognostic outcome. Subsequent screening enabled identification of 12 hub genes that constitute a CMLS with robust predictive power for prognosis. The low-CMLS group exhibited a more favorable prognosis and greater responsiveness to immunotherapy and was more likely to exhibit the "hot tumor" phenotype. The high-CMLS group had a poor prognosis and lower likelihood of benefitting from immunotherapy, but dasatinib and romidepsin may serve as promising treatments for them. Comprehensive analysis of multiomics data can offer important insights and further refine the molecular classification of MUC. Identification of CMLS represents a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice.

Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM

Background

Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations.

Results

An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM.

Conclusion

MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.

Immune response and drug therapy based on ac4C-modified gene in pancreatic cancer typing

N-4 cytidine acetylation (ac4C) is an epitranscriptome modification catalyzed by N-acetyltransferase 10 (NAT10) and is essential for cellular mRNA stability, rRNA biosynthesis, cell proliferation, and epithelial-mesenchymal transition (EMT). Numerous studies have confirmed the inextricable link between NAT10 and the clinical characteristics of malignancies. It is unclear, however, how NAT10 might affect pancreatic ductal adenocarcinoma. We downloaded pancreatic ductal adenocarcinoma patients from the TCGA database. We obtained the corresponding clinical data for data analysis, model construction, differential gene expression analysis, and the GEO database for external validation. We screened the published papers for NAT10-mediated ac4C modifications in 2156 genes. We confirmed that the expression levels and genomic mutation rates of NAT10 differed significantly between cancer and normal tissues. Additionally, we constructed a NAT10 prognostic model and examined immune infiltration and altered biological pathways across the models. The NAT10 isoforms identified in this study can effectively predict clinical outcomes in pancreatic ductal adenocarcinoma. Furthermore, our study showed that elevated levels of NAT10 expression correlated with gemcitabine resistance, that aberrant NAT10 expression may promote the angiogenic capacity of pancreatic ductal adenocarcinoma through activation of the TGF-β pathway, which in turn promotes distal metastasis of pancreatic ductal adenocarcinoma, and that NAT10 knockdown significantly inhibited the migration and clonogenic capacity of pancreatic ductal adenocarcinoma cells. In conclusion, we proposed a predictive model based on NAT10 expression levels, a non-invasive predictive approach for genomic profiling, which showed satisfactory and effective performance in predicting patients' survival outcomes and treatment response. Medicine and electronics will be combined in more interdisciplinary areas in the future.

Construction of noninvasive prognostic model of bladder cancer patients based on urine proteomics and screening of natural compounds

BACKGROUND

Bladder cancer (BCa) has a high incidence and recurrence rate worldwide. So far, there is no noninvasive detection of BCa therapy and prognosis based on urine multi-omics. Therefore, it is necessary to explore noninvasive predictive models and novel treatment modalities for BCa.

METHODS

First, we performed protein analysis of urine from five BCa patients and five healthy individuals using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Combining multi-omics data to mine particular and sensitive molecules to predict BCa prognosis. Second, urine proteomics data were combined with TCGA transcriptome data to select differential genes that were specifically highly expressed in urine and tissues. Further, the Lasso equation was used to screen specific molecules to construct a noninvasive prediction model of BCa. Finally, natural compounds of specific molecules were selected by combined network pharmacology and molecular docking to complete molecular structure docking.

RESULTS

A noninvasive predictive model was constructed using PSMB5, P4HB, S100A16, GET3, CNP, TFRC, DCXR, and MPZL1, specific molecules screened by multi-omics, and clinical features, which had good predictive value at 1, 3, and 5 years of prediction. High expression of these target genes suggests a poor prognosis in patients with BCa, and they were mainly involved in cell adhesion molecules and the IGF pathway. In addition, the corresponding drugs and natural compounds were selected by network pharmacology, and the molecular structure 7NHT of PSMB5 was found to be well docked to Ellagic acid, a natural compound in Hetaoren that we found. The 3D structure 6I7S of P4HB was able to bind to Stigmasterol in Shanzha stably, and the structure 6WRV of TFRC as an iron transport carrier was also able to bind to Stigmasterol in Shanzha stably. The structures 1WOJ, 3D3W, and 6IGW of CNP, DCXR, and MPZL1 can also play an important role in combination with the natural compounds (S)-Stylopine, Kryptoxanthin, and Sitosterol in Maqianzi, Yumixu, and Laoguancao.

CONCLUSION

The noninvasive prediction model based on urinomics had excellent potential in predicting the prognosis of patients with BCa. The multi-omics screening of specific molecules combined with pharmacology and compound molecular docking can promote the research and development of novel drugs.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

Identification of a DNA Damage Response and Repair-Related Gene-Pair Signature for Prognosis Stratification Analysis in Hepatocellular Carcinoma

Background: Nowadays, although the cause of hepatocellular carcinoma (HCC) mortality and recurrence remains at a high level, the 5-year survival rate is still very low. The DNA damage response and repair (DDR) pathway may affect HCC patients’ survival by influencing tumor development and therapeutic response. It is necessary to identify a prognostic DDR-related gene signature to predict the outcome of patients.

Methods: Level 3 mRNA expression and clinical information were extracted from the TCGA website. The GSE14520 datasets, ICGC-LIRI datasets, and a Chinese HCC cohort were served as validation sets. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were performed to construct the DDR-related gene pair (DRGP) signature. Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) analysis curves were calculated to determine the predictive ability of this prognostic model. Then, a prognostic nomogram was established to help clinical management. We investigated the difference in biological processes between HRisk and LRisk by conducting several enrichment analyses. The TIDE algorithm and R package “pRRophetic” were applied to estimate the immunotherapeutic and chemotherapeutic response.

Results: We constructed the prognostic signature based on 23 DDR-related gene pairs. The patients in the training datasets were divided into HRisk and LRisk groups at median cut-off. The HRisk group had significantly poorer OS than the LRisk group, and the signature was an independent prognostic indicator in HCC. Furthermore, a nomogram of the riskscore combined with TNM stage was constructed and detected by the calibration curve and decision curve. The LRisk group was associated with higher expression of HBV oncoproteins and metabolism pathways, while DDR-relevant pathways and cell cycle process were enriched in the HRisk group. Moreover, patients in the LRisk group may be more beneficial from immunotherapy. We also found that TP53 gene was more frequently mutated in the HRisk group. As for chemotherapeutic drugs commonly used in HCC, the HRisk group was highly sensitive to 5-fluorouracil, while the LRisk group presented with a significantly higher response to gefitinib and gemcitabine.

Conclusion: Overall, we developed a novel DDR-related gene pair signature and nomogram to assist in predicting survival outcomes and clinical treatment of HCC patients. It also helps understand the underlying mechanisms of different DDR patterns in HCC.