ARAF protein kinase activates RAS by antagonizing its binding to RASGAP NF1.

RAF protein kinases are effectors of the GTP-bound form of small guanosine triphosphatase RAS and function by phosphorylating MEK. We showed here that the expression of ARAF activated RAS in a kinase-independent manner. Binding of ARAF to RAS displaced the GTPase-activating protein NF1 and antagonized NF1-mediated inhibition of RAS. This reduced ERK-dependent inhibition of RAS and increased RAS-GTP. By this mechanism, ARAF regulated the duration and consequences of RTK-induced RAS activation and supported the RAS output of RTK-dependent tumor cells. In human lung cancers with EGFR mutation, amplification of ARAF was associated with acquired resistance to EGFR inhibitors, which was overcome by combining EGFR inhibitors with an inhibitor of the protein tyrosine phosphatase SHP2 to enhance inhibition of nucleotide exchange and RAS activation.

A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies.

KRAS mutant cancer, characterized by the activation of a plethora of phosphorylation signaling pathways, remains a major challenge for cancer therapy. Despite recent advancements, a comprehensive profile of the proteome and phosphoproteome is lacking. This study provides a proteomic and phosphoproteomic landscape of 43 KRAS mutant cancer cell lines across different tissue origins. By integrating transcriptomics, proteomics, and phosphoproteomics, we identify three subsets with distinct biological, clinical, and therapeutic characteristics. The integrative analysis of phosphoproteome and drug sensitivity information facilitates the identification of a set of drug combinations with therapeutic potentials. Among them, we demonstrate that the combination of DOT1L and SHP2 inhibitors is an effective treatment specific for subset 2 of KRAS mutant cancers, corresponding to a set of TCGA clinical tumors with the poorest prognosis. Together, this study provides a resource to better understand KRAS mutant cancer heterogeneity and identify new therapeutic possibilities.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.

Predicting circRNA-drug sensitivity associations by learning multimodal networks using graph auto-encoders and attention mechanism.

Recent studies have shown that the expression of circRNAs would affect drug sensitivity of cells and thus significantly influence the efficacy of drugs. Traditional biomedical experiments to validate such relationships are time-consuming and costly. Therefore, developing effective computational methods to predict potential associations between circRNAs and drug sensitivity is an important and urgent task. In this study, we propose a novel method, called MNGACDA, to predict possible circRNA-drug sensitivity associations for further biomedical screening. First, MNGACDA uses multiple sources of information from circRNAs and drugs to construct multimodal networks. It then employs node-level attention graph auto-encoders to obtain low-dimensional embeddings for circRNAs and drugs from the multimodal networks. Finally, an inner product decoder is applied to predict the association scores between circRNAs and drug sensitivity based on the embedding representations of circRNAs and drugs. Extensive experimental results based on cross-validations show that MNGACDA outperforms six other state-of-the-art methods. Furthermore, excellent performance in case studies demonstrates that MNGACDA is an effective tool for predicting circRNA-drug sensitivity associations in real situations. These results confirm the reliable prediction ability of MNGACDA in revealing circRNA-drug sensitivity associations.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration.

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2, CEBPZOS, LDHA, and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.

Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia.

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1-G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6-G8, showing high expression of HOXA/B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX-primitive (G7), HOX-mixed (G8), and HOX-committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics.

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.

BACKGROUND: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. RESULTS: This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results. CONCLUSIONS: Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).

Multifaceted role of FAM210B in hepatocellular carcinoma: Implications for tumour progression, microenvironment modulation and therapeutic selection.

Hepatocellular carcinoma (HCC) is a common and lethal liver cancer characterized by complex aetiology and limited treatment options. FAM210B, implicated in various cancers, is noteworthy for its potential role in the progression and treatment response of HCC. Yet, its expression patterns, functional impacts and correlations with patient outcomes and resistance to therapy are not well understood. We employed a comprehensive methodology to explore the role of FAM210B in HCC, analysing its expression across cancers, subcellular localization and impacts on cell proliferation, invasion, migration, biological enrichment and the immune microenvironment. Additionally, we investigated its expression in single cells, drug sensitivity and relationships with genomic instability, immunotherapy efficacy and key immune checkpoints. While FAM210B expression varied across cancers, there was no notable difference between HCC and normal tissues. Elevated levels of FAM210B were associated with improved survival outcomes. Subcellular analysis located FAM210B in the plasma membrane and cytosol. FAM210B was generally downregulated in HCC, and its suppression significantly enhanced cell proliferation, invasion and migration. Biological enrichment analysis linked FAM210B to metabolic and immune response pathways. Moreover, its expression modified the immune microenvironment of HCC, affecting drug responsiveness and immunotherapy outcomes. High expression levels of FAM202B correlated with increased resistance to sunitinib and enhanced responsiveness to immunotherapy, as evidenced by associations with tumour mutation burden, PDCD1, CTLA4 and TIDE scores. FAM210B exerts a complex influence on HCC, affecting tumour cell behaviour, metabolic pathways, the immune microenvironment and responses to therapy.

IGFBP7 regulates cell proliferation and migration through JAK/STAT pathway in gastric cancer and is regulated by DNA and RNA methylation.

New biomarkers for early diagnosis of gastric cancer (GC), the second leading cause of cancer-related death, are urgently needed. IGFBP7, known to play various roles in multiple tumours, is complexly regulated across diverse cancer types, as evidenced by our pancancer analysis. Bioinformatics analysis revealed that IGFBP7 expression was related to patient prognosis, tumour clinicopathological characteristics, tumour stemness, microsatellite instability and immune cell infiltration, as well as the expression of oncogenes and immune checkpoints. GSEA links IGFBP7 to several cancer-related pathways. IGFBP7 deficiency inhibited GC cell proliferation and migration in vitro. Furthermore, an in vivo nude mouse model revealed that IGFBP7 downregulation suppressed the tumorigenesis of GC cells. Western blotting analysis showed that the JAK1/2-specific inhibitor ruxolitinib could rescue alterations induced by IGFBP7 overexpression in GC cells. Additionally, our bioinformatics analysis and in vitro assays suggested that IGFBP7 is regulated by DNA methylation at the genetic level and that the RNA m6A demethylase FTO modulates it at the posttranscriptional level. This study emphasizes the clinical relevance of IGFBP7 in GC and its influence on cell proliferation and migration via the JAK/STAT signalling pathway. This study also highlights the regulation of IGFBP7 in GC by DNA and m6A RNA methylation.

Construction of endothelial cell signatures for predicting the diagnosis, prognosis and immunotherapy response of bladder cancer via machine learning.

We subtyped bladder cancer (BC) patients based on the expression patterns of endothelial cell (EC) -related genes and constructed a diagnostic signature and an endothelial cell prognostic index (ECPI), which are useful for diagnosing BC patients, predicting the prognosis of BC and evaluating drug sensitivity. Differentially expressed genes in ECs were obtained from the Tumour Immune Single-Cell Hub database. Subsequently, a diagnostic signature, a tumour subtyping system and an ECPI were constructed using data from The Cancer Genome Atlas and Gene Expression Omnibus. Associations between the ECPI and the tumour microenvironment, drug sensitivity and biofunctions were assessed. The hub genes in the ECPI were identified as drug candidates by molecular docking. Subtype identification indicated that high EC levels were associated with a worse prognosis and immunosuppressive effect. The diagnostic signature and ECPI were used to effectively diagnose BC and accurately assess the prognosis of BC and drug sensitivity among patients. Three hub genes in the ECPI were extracted, and the three genes had the closest affinity for doxorubicin and curcumin. There was a close relationship between EC and BC. EC-related genes can help clinicians diagnose BC, predict the prognosis of BC and select effective drugs.

Differential genetic mutations and immune cell infiltration in high- and low-risk STAD: Implications for prognosis and immunotherapy efficacy.

This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA-STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low-risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame-shift mutations more prevalent in the low-risk group and missense mutations in the high-risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high-risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high-risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high-risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision-making in gastric cancer management.

Colorectal cancer subtyping and prognostic model construction based on interleukin-related genes.

Members of the interleukin (IL) family are closely linked to cancer development and progression. However, research on the prognosis of colorectal cancer (CRC) related to IL is still lacking. This study investigated new CRC prognostic markers and offered new insights for CRC prognosis and treatment. CRC-related data and IL gene data were collected from public databases. Sample clustering was done with the NMF package to divide samples into different subtypes. Differential, enrichment, survival, and immune analyses were conducted on subtypes. A prognostic model was constructed using regression analysis. Drug sensitivity analysis was performed using GDSC database. Western blot analysis was performed to assess the effect of IL-7 on the JAK/STAT signaling pathway. Flow cytometry was used to examine the impact of IL-7 on CD8+ T cell apoptosis. Two CRC subtypes based on IL-associated genes were obtained. Cluster 1 had a higher survival rate than cluster 2, and they showed differences in some immune levels. The two clusters were mainly enriched in the JAK-STAT signaling pathway, T helper 17 cell differentiation, and the IL-17 signaling pathway. An 11-gene signature was built, and risk score was an independent prognosticator for CRC. The low-risk group showed a higher sensitivity to nine common targeted anticancer drugs. Western blot and flow cytometry results demonstrated that IL-7 could phosphorylate STAT5 and promote survival of CD8+ T cells. In conclusion, this study divided CRC samples into two IL-associated subtypes and obtained an 11-gene signature. In addition, targeted drugs that may improve the prognosis of patients with CRC were identified. These findings are of paramount importance for patient prognosis and CRC treatment.NEW & NOTEWORTHY We identified two clusters with significant survival differences in colorectal cancer (CRC) based on interleukin-related genes, constructed an 11-gene risk score model that can independently predict the prognosis of CRC, and explored some targeted drugs that may improve the prognosis of patients with CRC. The results of this study have important implications for the prognosis and treatment of CRC.

Unveiling the therapeutic prospects of IFNW1 and IFNA21: insights into glioma pathogenesis and clinical significance.

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients.

Molecular prognostic of nine parthanatos death-related genes in glioma, particularly in COL8A1 identification.

Post-operative progression and chemotherapy resistance are the main causes of treatment failure in glioma patients. There is a lack of ideal prediction models for post-operative glioma patient progression and drug sensitivity. We aimed to develop a prognostic model of parthanatos mRNA biomarkers for glioma outcomes. A total of 11 parthanatos genes were obtained from ParthanatosCluster database. ConsensusClusterPlus and R "Limma" package were used to cluster The Cancer Genome Atlas (TCGA)-glioma cohort and analyze the differential mRNAs. Univariate Cox regression analysis, random survival forest model, and least absolute shrinkage and selection operator (LASSO) regression analysis were used to determine the nine ParthanatosScore prognostic genes combination. ParthanatosScore was verified by 656 patients and 979 patients in TCGA and CGCA-LGG/GBM datasets. Differences in genomic mutations, tumor microenvironments, and functional pathways were assessed. Drug response prediction was performed using pRRophetic. Kaplan-Meier survival analysis was analyzed. Finally, COL8A1 was selected to evaluate its potential biological function and drug sensitivity of temozolomide and AZD3759 in glioma cells. ParthanatosScore obtained a combination of nine glioma prognostic genes, including CD58, H19, TNFAIP6, FTLP3, TNFRSF11B, SFRP2, LOXL1, COL8A1, and FABP5P7. In the TCGA-LGG/GBM dataset, glioma prognosis was poor in high ParthanatosScore. Low-score glioma patients were sensitive to AZD3759_1915, AZD5582_1617, AZD8186_1918, Dasatinib_1079, and Temozolomide_1375, while high-score patients were less sensitive to these drugs. Compared with HA cells, COL8A1 was significantly over-expressed in LN229 and U251 cells. Silencing COL8A1 inhibited the malignant characterization of LN229 and U251 cells. Temozolomide and AZD3759 also promoted parthanatos gene expression in glioma cells. Temozolomide and AZD3759 inhibited COL8A1 expression and cell viability and promoted apoptosis in glioma cells and PGM cells. ParthanatosScore can accurately predict clinical prognosis and drug sensitivity after glioma surgery. Silencing COL8A1 inhibited the malignant characterization. Temozolomide and AZD3759 inhibited COL8A1 expression and cell viability and promoted apoptosis and parthanatos gene expression, which is a target to improve glioma.

Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients.

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

The systematic identification of survival-related alternative splicing events and splicing factors in glioblastoma.

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor, making it one of the most life-threatening human cancers. Nevertheless, research on the mechanism of action between alternative splicing (AS) and splicing factor (SF) or biomarkers in GBM is limited. AS is a crucial post-transcriptional regulatory mechanism. More than 95% of human genes undergo AS events. AS can diversify the expression patterns of genes, thereby increasing the diversity of proteins and playing a significant role in the occurrence and development of tumors. In this study, we downloaded 599 clinical data and 169 transcriptome analysis data from The Cancer Genome Atlas (TCGA) database. Besides, we collected AS data about GBM from TCGA-SpliceSeq. The overall survival (OS) related AS events in GBM were determined through least absolute shrinkage and selection operator (Lasso) and Cox analysis. Subsequently, the association of these 1825 OS-related AS events with patient survival was validated using the Kaplan-Meier survival analysis, receiver operating characteristic curve, risk curve analysis, and independent prognostic analysis. Finally, we depicted the AS-SF regulatory network, illustrating the interactions between splicing factors and various AS events in GBM. Additionally, we identified three splicing factors (RNU4-1, SEC31B, and CLK1) associated with patient survival. In conclusion, based on AS occurrences, we developed a predictive risk model and constructed an interaction network between GBM-related AS events and SFs, aiming to shed light on the underlying mechanisms of GBM pathogenesis and progression.