Genomics Of Drug Sensitivity In Cancer Research Articles

Small nucleolar RNA host gene 1 (SNHG1) facilitates gemcitabine chemosensitivity in gallbladder cancer by regulating the miR-23b-3p/phosphatase and tensin homolog (PTEN) pathway.

Growing evidence suggests that long non-coding RNAs (lncRNAs) exert pivotal roles in fostering chemoresistance across diverse tumors. Nevertheless, the precise involvement of lncRNAs in modulating chemoresistance within the context of gallbladder cancer (GBC) remains obscure. This study aimed to uncover how lncRNAs regulate chemoresistance in gallbladder cancer, offering potential targets to overcome drug resistance. To elucidate the relationship between gemcitabine sensitivity and small nucleolar RNA host gene 1 (SNHG1) expression, we utilized publicly available GBC databases, GBC tissues from Renji Hospital collected between January 2017 and December 2019, as well as GBC cell lines. The assessment of SNHG1, miR-23b-3p, and phosphatase and tensin homolog (PTEN) expression was performed using in situ hybridization, quantitative real-time polymerase chain reaction, and western blotting. The cell counting kit-8 (CCK-8) assay was used to quantify the cell viability. Furthermore, a GBC xenograft model was employed to evaluate the impact of SNHG1 on the therapeutic efficacy of gemcitabine. Receiver operating characteristic (ROC) curve analyses were executed to assess the specificity and sensitivity of SNHG1. Our analyses revealed an inverse correlation between the lncRNA SNHG1 and gemcitabine resistance across genomics of drug sensitivity in cancer (GDSC) and Gene Expression Omnibus (GEO) datasets, GBC cell lines, and patients. Gain-of-function investigations underscored that SNHG1 heightened the gemcitabine sensitivity of GBC cells in both in vitro and in vivo settings. Mechanistic explorations illuminated that SNHG1 could activate PTEN-a commonly suppressed tumor suppressor gene in cancers-thereby curbing the development of gemcitabine resistance in GBC cells. Notably, microRNA (miRNA) target prediction algorithms unveiled the presence of miR-23b-3p binding sites within SNHG1 and the 3'-untranslated region (UTR) of PTEN. Moreover, SNHG1 acted as a sponge for miR-23b-3p, competitively binding to the 3'-UTR of PTEN, thereby amplifying PTEN expression and heightening the susceptibility of GBC cells to gemcitabine. The SNHG1/miR-23b-3p/PTEN axis emerges as a pivotal regulator of gemcitabine sensitivity in GBC cells, holding potential as a promising therapeutic target for managing GBC patients.

CTHRC1 expresses in cancer-associated fibroblasts and is associated with resistance to anti-androgen therapy in prostate cancer.

CTHRC1 overexpresses in prostate cancer and is associated with the proliferation, invasion and migration of prostate cancer cells. However, the roles and mechanisms of CTHRC1 expression in prostate cancer prognosis and treatment outcomes remain unknown. This study aimed to explore the expression and gene function of CTHRC1 in prostate cancer, investigate the prognostic value and potential effect in the treatment of prostate cancer. Bulk and single-cell RNA sequencing analyses were used to evaluate the expression of CTHRC1 in prostate cancer. All data used in the study were obtained from publicly available sources to ensure transparency. Study enrolled 1999 cases of prostate cancer and 531 normal controls. Single-cell RNA sequencing profile included 62,995 cells from seven prostate primary tumors. CTHRC1 expression and prognosis analyses were conducted with these samples and verified by immunohistochemical staining. CIBERSORT algorithm was used to assess the tumor immune infiltrating cells based on bulk mRNA sequencing profiles. Genomics of drug sensitivity in cancer (GDSC) database was used to predict IC50 to anti-androgen therapy (ADT) drugs of the samples. CTHRC1 expressed in prostate cancer was higher than that in normal prostate tissue, and the expression increased with the progress of prostate cancer. CTHRC1 was the risk factor of progression-free interval (PFI). CTHRC1 was positively correlated with the infiltration of tumor-associated macrophages (TAMs). Myofibroblast-like cancer-associated fibroblasts (myCAFs) were the major CTHRC1 expressers in prostate cancer. TGF-β signaling activated in CTHRC1-positive myCAFs and was involved in TAMs polarization. Biological functions of myCAFs were enriched in hormone response and metabolism. CTHRC1 may regulate androgen receptor signaling through CCN2/CAV1/AR pathway. Moreover, ADT drug Bicalutamide and AZD3514 were less sensitive in the high CTHRC1 expression tumors. As a potential molecular target of ADT resistance in prostate cancer, CTHRC1 provides a new promising molecular approach for the diagnosis and treatment of prostate cancer.

Pharmacogenomics-based subtype decoded implications for risk stratification and immunotherapy in pancreatic adenocarcinoma

BackgroundWith fatal malignant peculiarities and poor survival rate, outcomes of pancreatic adenocarcinoma (PAAD) were frustrated by non-response and even resistance to therapy due to heterogeneity across clinical patients. Nevertheless, pharmacogenomics has been developed for individualized-treatment and still maintains obscure in PAAD.MethodsA total of 964 samples from 10 independent multi-center cohorts were enrolled in our study. With drug response data from the profiling of relative inhibition simultaneously in mixtures (PRISM) and genomics of drug sensitivity in cancer (GDSC) databases, we established and validated multidimensionally three pharmacogenomics-classified subtypes using non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms, separately. The heterogenous biological characteristics and precision medicine strategies among subtypes were further investigated.ResultsThree pharmacogenomics-classified subtypes after stable and reproducible validation, distinguished in six aspects of prognosis, biological peculiarities, immune landscapes, genomic variations, immunotherapy and individualized management strategies. Subtype 2 was close to immunocompetent phenotype and projected to immunotherapy; Subtype 3 held most favorable outcomes and metabolic pathways distinctively, promising to be treated with first-line agents. Subtype 1 with worst prognosis, was anticipated to chromosome instability (CIN) phenotype and resistant to chemotherapeutic agents. In addition, ITGB6 contributed to subtype 1 resistance to 5-fluorouracil, and knockdown of ITGB6 enhanced sensitivity to 5-fluorouracil in in vitro experiments. Ultimately, appropriate clinical stratified treatments were assigned to corresponding subtypes according to pharmacogenomic transcripts. Some limitations were not taken into account, thus needs to be supported by more research.ConclusionA span-new molecular subtype exploited for PAAD uncovered an insight into precise medication on ground of pharmacogenomics, and highly refined multiple clinical management strategies for specific patients.

Comprehensive characterization of fatty acid oxidation in triple-negative breast cancer: Focus on biological roles and drug modulation.

Comprehensive characterization of fatty acid oxidation in triple-negative breast cancer: Focus on biological roles and drug modulation.

NIPAL1 as a prognostic biomarker associated with pancreatic adenocarcinoma progression and immune infiltration

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor in the digestive system, with an increasing incidence and mortality rate globally. Recent genetic studies have revealed that the abnormal expression and functional dysregulation of various genes are involved in the occurrence and progression of pancreatic cancer. NIPA-like proteins (NIPAs) are expressed in a variety of cancer types, yet the role of NIPAL1 in cancer remains unclear. Therefore, further research is required to determine its diagnostic significance and understand its biological functions in cancer. Primitive RNA sequencing (RNA-seq) data of PAAD from The Cancer Genome Atlas (TCGA) was utilized for bioinformatics analysis to characterize the expression levels of NIPAL1 in tumor and normal tissues. Differentially expressed genes (DEGs) were identified, and Gene Set Enrichment Analysis (GSEA) was performed to elucidate potential biological mechanisms of NIPAL1 involved in PAAD development. Additionally, we analyzed the correlation between NIPAL1 expression, immune cell infiltration, and PAAD progression. The Genomics of Drug Sensitivity in Cancer (GDSC) database was utilized to investigate the relationship between NIPAL1 expression and the efficacy of common drugs used in chemotherapy and targeted therapy in patients with pancreatic cancer. Subsequently, we predicted five small-molecule drugs targeted at NIPAL1 using molecular docking. Finally, high expression of NIPAL1 in tumor tissues was validated through immunohistochemistry. In pancreatic cancer cell lines, changes in phenotypes such as proliferation, migration, and invasion following the knockdown of NIPAL1 were assessed. Finally, we established a subcutaneous tumor-bearing mouse model to further validate its therapeutic significance in vivo.

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function

BackgroundAdvanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC.MethodsTranscriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation.ResultsLower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness.ConclusionsOur prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Comparative analysis of regression algorithms for drug response prediction using GDSC dataset

BackgroundDrug response prediction can infer the relationship between an individual’s genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient. In addition, it is difficult for researchers to know which algorithm is appropriate for prediction as various regression and feature selection algorithms exist.MethodsWe compared and evaluated the performance of 13 representative regression algorithms using Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Three analyses was conducted to show the effect of feature selection methods, multiomics information, and drug categories on drug response prediction.ResultsIn the experiments, Support Vector Regression algorithm and gene features selected with LINC L1000 dataset showed the best performance in terms of accuracy and execution time. However, integration of mutation and copy number variation information did not contribute to the prediction. Among the drug groups, responses of drugs related with hormone-related pathway were predicted with relatively high accuracy.ConclusionThis study can help bioinformatics researchers design data processing steps and select algorithms for drug response prediction, and develop a new drug response prediction model based on the GDSC or other high-throughput sequencing datasets.

Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.

Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes. A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC50 values for 542 tumor cell lines across 18 drugs. The model exhibited high predictive accuracy, with correlation coefficients (R) of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel (R = 0.72) and Cisplatin (R = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate (R < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations (R = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, P = 0.007, AUC = 0.793). The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC50 and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.

Two-Step Transfer Learning Improves Deep Learning–Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma

While deep learning (DL) is used in patients’ outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures. The GBM is aggressive, and most patients do not benefit from the only approved chemotherapeutic agent TMZ. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is the only biomarker for TMZ responsiveness but has shown limited predictive power. The 2-step TL framework was built on 3 datasets: (1) the subset of the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, including miscellaneous cell cultures treated by TMZ, cyclophosphamide, bortezomib, and oxaliplatin, as the source dataset; (2) the Human Glioblastoma Cell Culture (HGCC) dataset, for fine-tuning; and (3) a small target dataset GSE232173, for validation. The latter two included specifically TMZ-treated GBM cell cultures. The DL models were pretrained on the cell cultures treated by each of the 4 drugs from GDSC, respectively. Then, the DL models were refined on HGCC, where the best source drug was identified. Finally, the DL model was validated on GSE232173. Using 2-step TL with pretraining on oxaliplatin was not only superior to those without TL and with 1-step TL but also better than 3 benchmark methods, including MGMT. The oxaliplatin-based TL improved the performance probably by increasing the weights of cell cycle-related genes, which relates to the TMZ response processes. Our findings support the potential of oxaliplatin being an alternative therapy for patients with GBM and TL facilitating drug repurposing research. We recommend that following our methodology, using mixed cancers and a related drug as the source and then fine-tuning the model with the target cancer and the target drug will enhance drug response prediction.

KLRD1 (CD94): A Prognostic Biomarker and Therapeutic Candidate in Head and Neck Squamous Cell Carcinoma.

Background: Killer Cell Lectin Like Receptor D1 (KLRD1) plays a crucial role in antitumor immunity. However, its expression patterns across various cancers, its relationship with patient prognosis, and its potential as an immunotherapy target remain inadequately understood. Methods: We analyzed KLRD1 expression across various cancer types using multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, correlating it with patient prognosis. Single-cell RNA sequencing data were employed to further explore KLRD1 expression in natural killer (NK) cells and exhausted CD8+ T cells (CD8Tex). Functional enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) identified the biological processes and pathways associated with KLRD1. Immune infiltration analysis, conducted via CIBERSORT, assessed the relationship between KLRD1 expression and immune cell infiltration within the tumor microenvironment. Furthermore, the Tracking Tumor Immunophenotype (TIP) meta-server and Easier tool were employed to assess the role of KLRD1 in the cancer immunity cycle and to predict immunotherapy responses. Drug sensitivity was predicted using tools like CellMiner and the Genomics of Drug Sensitivity in Cancer (GDSC) database to explore the link between KLRD1 expression and responsiveness to various anticancer drugs. Results: KLRD1 exhibits significant differential expression and strong prognostic value across cancers, particularly as an independent prognostic factor in head and neck squamous cell carcinoma (HNSC). Single-cell analysis revealed high expression of KLRD1 in NK and CD8Tex cells, indicating its critical role in antitumor immune responses. Functional enrichment analyses showed that KLRD1 is involved in several immune-related signaling pathways, including NK cell-mediated cytotoxicity and T cell receptor pathways. Immune infiltration analysis further confirmed a positive correlation between KLRD1 expression and the infiltration of various immune cells. Moreover, higher KLRD1 expression in HNSC is associated with enhanced immune pathway activity, increased sensitivity to cell division inhibitors, and the identification of arachidonyltrifluoromethane as a potential compound to counteract its oncogenic effects. Conclusion: In HNSC, KLRD1 is a key prognostic marker and potential target for personalized immunotherapy.

Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.

Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.

Pan-gastrointestinal adenocarcinoma analysis uncovers the prognostic and immune correlates of ferroptosis-related genes.

Gastrointestinal adenocarcinomas (GIACs) are common malignant tumors with poor prognosis in the world. Ferroptosis, characterized by the accumulation of intracellular iron and lipid reactive oxygen species, emerges as a pivotal process in tumorigenesis and cancer advancement. However, the implications of ferroptosis-related genes in GIAC remain to be elucidated. This study aimed at exploring the potential role of ferroptosis-related genes on the prognosis and treatment of GIAC. In our study, comprehensive clinical, transcriptomic, and/or genomic data were acquired from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC), and Gene Expression Omnibus (GEO). We formulated a ferroptosis-score within the TCGA cohort through gene set variation analysis (GSVA) and subsequently validated in 4 GEO datasets (GSE84437, GSE17536, GSE103479, and GSE19417). Drug sensitivity and immunotherapy efficacy were analyzed in the GDSC dataset and the PRJEB25780 cohort, respectively. The ferroptosis-score was significantly associated with favorable overall survival both in the training cohort [TCGA: P=0.003; hazard ratio (HR), 0.67, 95% confidence interval (95% CI): 0.52-0.87] and across the four validation cohorts (GSE17536: P=0.03; HR, 0.57, 95% CI: 0.34-0.96; GSE19417: P=0.047; HR, 0.53, 95% CI: 0.28-1.01; GSE84437: P=0.004; HR, 0.68, 95% CI: 0.51-0.90; GSE103479: P=0.03; HR, 0.55, 95% CI: 0.32-0.96). Furthermore, the ferroptosis-score was correlated with activation of the DNA damage repair pathway and resistance to cisplatin. Notably, GIACs with low ferroptosis-scores exhibited heightened expression of immune checkpoint molecules such as programmed death-(ligand) 1 and cytotoxic T lymphocyte antigen-4, elevated densities of tumor-infiltrating CD8+ T cells, and a favorable response to pembrolizumab monotherapy. Our findings delineated the clinical relevance of ferroptosis-related genes in GIACs and demonstrated the potential utility of the ferroptosis-score in predicting prognosis and immunotherapy effectiveness.

Machine learning-based identification of histone deacetylase-associated prognostic factors and prognostic modeling for low-grade glioma.

Low-grade glioma (LGG) is a slow-growing but invasive tumor that affects brain function. Histone deacetylases (HDACs) play a critical role in gene regulation and tumor progression. This study aims to develop a prognostic model based on HDAC-related genes to aid in risk stratification and predict therapeutic responses. Expression data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify an optimal HDAC-related risk signature from 73 genes using 10 machine learning algorithms. Patients were stratified into high- and low-risk groups based on the median risk score. Prognostic accuracy was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), were performed to explore pathways linked to the gene signature. Immune infiltration and tumor microenvironment characteristics were assessed using Single Sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE algorithm. SubMap was applied to predict responsiveness to immune checkpoint inhibitors, and chemotherapeutic sensitivity was analyzed via the Genomics of Drug Sensitivity in Cancer (GDSC) database. A prognostic model consisting of four HDAC-related genes-SP140, BAZ1B, SP100, and SIRT1-was identified. This signature displayed strong prognostic accuracy, achieving a C-index of 0.945. Individuals with LGG were systematically divided into high-risk and low-risk cohorts based on the median risk value, enabling more precise risk stratification. The survival prognosis was significantly worse in the high-risk cohort compared to the low-risk group, highlighting distinct survival trajectories. Notably, the two cohorts exhibited marked shifts in immune checkpoint gene transcriptional profiles and immune cell infiltration maps, underscoring fundamental biological differences that contribute to these differing prognoses. We developed an HDAC-related four-gene prognostic model that correlates with survival, immune landscape, and therapeutic response in LGG patients. This model may guide personalized treatment strategies and improve prognostic accuracy, warranting further validation in clinical settings.

Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

ObjectivesBladder cancer (BLCA) is a tumor that affects men more than women. The biological function and prognostic value of androgen-responsive genes (ARGs) in BLCA are currently unknown. To address this, we established an androgen signature to determine the prognosis of BLCA.MethodsSequencing data for BLCA from the TCGA and GEO datasets were used for research. The tumor microenvironment (TME) was measured using Cibersort and ssGSEA. Prognosis-related genes were identified and a risk score model was constructed using univariate Cox regression, LASSO regression, and multivariate Cox regression. Drug sensitivity analysis was performed using Genomics of drug sensitivity in cancer (GDSC). Real-time quantitative PCR was performed to assess the expression of representative genes in clinical samples.ResultsARGs (especially the CDK6, FADS1, PGM3, SCD, PTK2B, and TPD52) might regulate the progression of BLCA. The different expression patterns of ARGs may lead to different immune cell infiltration. The risk model indicates that patients with higher risk scores have a poorer prognosis, more stromal infiltration, and an enrichment of biological functions. Single-cell RNA analysis, bulk RNA data, and PCR analysis support the reliability of this risk model, and a nomogram was also established for clinical use. Drug prediction analysis showed that high-risk patients had a better response to fludarabine, AZD8186, and carmustine.ConclusionARGs played an important role in the progression, immune infiltration, and prognosis of BLCA. The ARGs model has high accuracy in predicting the prognosis of BLCA patients and provides more effective medication guidelines.

An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes

An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes

Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches.

One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC50 values. Next, ML models, including support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), were trained on ChEMBL data to classify compounds with similar activity to the reference drug as active or inactive. The promising candidates underwent computational structural biology assessments for their molecular interactions and conformational dynamics. Our findings indicate that compounds ChEMBL233324, ChEMBL233325, ChEMBL234580, and ChEMBL372692 exhibit potent repressive action against EGFR, underscoring their potential as active antibreast cancer agents.

Machine learning-based pan-cancer study of classification and mechanism of BRAF inhibitor resistance.

V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim of the study was to develop a machine-learning based method to identify patients who are at high-risk of BRAFi resistance and potential biomarker. From Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases, we collected RNA sequencing and half maximal inhibitory concentration (IC50) data from 235 pan-cancer cell lines and then identified 37 significant differential expression genes associated with BRAFi resistance. Employing machine learning (ML) models, we successfully classified cell lines into resistant and sensitive groups, achieving robust performance in external validation datasets. AOX1 may play a vital part in BRAFi metabolism and resistance. Further, we found that higher mRNA expression of OXTR, H2AC13, and TBX2, and lower mRNA of SLC2A4, as detected by PCR in WM983B and SKMEL-5 cell lines, were independent risk factors for BRAFi resistance and were associated with poor prognosis. We established a gene-expression model using ML methods, consisting of 37 variables based on RNA-seq database, which was externally validated and could be used to predict BRAFi resistance. Meanwhile, our findings provide valuable insights into the molecular mechanisms of BRAFi resistance, enabling the identification of high-risk patients.

Copper metabolism-related lncRNAs predict prognosis and immune landscape in liver cancer patients.

Characterized by its high mortality and easy recurrence, hepatocellular carcinoma (HCC) poses significant clinical challenges. The association between copper metabolism and development of cancer has been identified. However, the underlying mechanisms of copper metabolism-related long non-coding RNAs (CMRLs) in HCC remain elusive. To address the gap, our study analyzed the prognostic and immuno-therapeutic value of CMRLs in HCC. This research utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data (n=424) for analysis, applying the "limma" package in R software for differential gene analysis and construction of a prognostic signature. We validated the signature using training and validation groups stochastically divided at a ratio of 1:1 and assessed prognostic value via Kaplan-Meier, C-index, and receiver operating characteristic (ROC) curves. By multivariate Cox regression, independent prognostic indicators were identified, and a nomogram was formulated for survival forecasting. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses elucidated biological pathways, and the immune landscape was examined through multiple algorithms. Finally, drug sensitivity was determined from Genomics of Drug Sensitivity in Cancer (GDSC), with mutation analysis conducted via maftools. In this study, a predictive model based on four pivotal CMRLs (PRRT3-AS1, AC108752.1, AC092115.3, AL031985.3) significantly associated with HCC progression and prognosis was constructed and validated with the overall survival (OS) prediction area under the curve (AUC) values for 1, 3, and 5 years of 0.718, 0.688, and 0.669, respectively. The calibration curves and C-index values showed a solid prognostic ability of the nomogram. The high-risk group was notably higher than the low-risk group both in OS and tumor mutational burdens (TMBs). Moreover, functional annotation enrichment analysis of CMRLs revealed that the signature was mainly associated with mitotic function, chromosome, kinetochore, cell cycle, and oocyte meiosis. Furthermore, therapeutic drugs, including fluorouracil, afatinib, alpelisib, cedranib, crizotinib, erlotinib, gefitinib, and ipatasertib, were found to induce higher sensitivity in high-risk group. The prognostic signature consisting of four CMRLs displays an outstanding predictive performance and improves the precision of immuno-oncology.

Construction of a disulfidptosis-related lncRNAs signature of the subtype, prognostic, and immunotherapy in neuroblastoma.

Disulfidptosis is an emerging form of regulated cell death distinguished by abnormal disulfide stress and the collapse of the actin network. This study was to construct a prognostic model based on disulfidptosis-related lncRNAs (DRLs) to enhance survival prediction and assess their viability as biomarkers for immunotherapy response in neuroblastoma (NB). Transcriptomic and clinical data from NB patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. DRLs linked to overall survival (OS) were identified using Pearson correlation and univariate Cox regression analyses. Molecular subtypes of NB were determined through consensus clustering. Immune cell infiltration was assessed with multiple algorithms. A prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Tumor mutational burden (TMB) analysis on somatic mutations from the TARGET database explored the TMB and risk score relationship. Patient responses to immunotherapy and anti-tumor drugs were predicted using tumor immune dysfunction and exclusion (TIDE), Tumor Inflammation Signature (TIS), Genomics of Drug Sensitivity in Cancer (GDSC) database, and CellMiner tools. We identified 151 DRLs associated with OS and defined three distinct DRLs subtypes. Using eight of these, we created a prognostic model. This model was proven independently significant and divided NB patients into high and low-risk groups. The high-risk group showed poorer OS, reduced immune cell presence and infiltration, and weaker response to immunotherapy. Conversely, the low-risk group demonstrated potential immunotherapy effectiveness and increased sensitivity to anti-tumor drugs. We established a prognostic model based on DRLs to predict the prognosis of NB patients, assess the immune cell infiltration, analyze TMB, evaluate the effectiveness of immunotherapy, and gauge sensitivity to anti-tumor drug treatments.

Emerging functions of FMNL1 in myeloid neoplasms: insights from bioinformatics to biological and pharmacological landscapes.

Myeloid neoplasms encompass disorders characterized by abnormal myeloid cell proliferation and differentiation, including myelodysplastic syndromes (MDS), myeloproliferative neoplasms, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). Formin-like protein 1 (FMNL1) is involved in the regulation of the actin cytoskeleton and is predominantly expressed in hematopoietic cells. Given its role in leukemia cell proliferation, survival, migration, and invasion, this study investigates FMNL1 expression in normal hematopoiesis and myeloid neoplasms and explores associations with clinical-laboratory characteristics, mutational status, and survival outcomes in AML. Transcript levels of FMNL1 from several blood-forming cell populations and myeloid neoplasms were extracted from publicly available databases. Myeloid neoplasm cell lines were used for gene/protein expression and cell differentiation studies. Functional genomics analysis was performed using RNA-seq data from The Cancer Genome Atlas (TCGA) AML study, and drug sensitivity predictions were investigated using Beat AML and Genomics of Drug Sensitivity in Cancer (GDSC) datasets. Statistical analyses assessed the impact of FMNL1 expression on clinical outcomes. FMNL1 was highly expressed in metamyelocytes, neutrophils, and monocytes compared to hematopoietic stem cells, and its expression increased with granulocytic differentiation. FMNL1 expression was elevated in AML and CML patients compared to healthy donors. FMNL1 expression was not significantly associated with clinical-laboratory characteristics or survival outcomes but showed a higher frequency of WT1 transcription factor (WT1) mutations with low FMNL1 expression in AML patients. High FMNL1 expression in AML correlated with immune response and inflammatory activity pathways. FMNL1 mRNA levels influenced drug sensitivity in AML models, with correlations observed for specific antineoplastic agents. FMNL1 plays a potential role in granulocyte differentiation and function, and its differential expression is linked to critical signaling pathways in leukemogenesis and inflammation. These findings highlight FMNL1's potential therapeutic implications in myeloid neoplasia, warranting further investigation.