Genomics Of Drug Sensitivity In Cancer Research Articles

Expression and Regulation Network of HDAC3 in Acute Myeloid Leukemia and the Implication for Targeted Therapy Based on Multidataset Data Mining.

Background Histone deacetylase 3 (HDAC3) plays an important role in the development and progression of a variety of cancers, but its regulatory mechanism in acute myeloid leukemia (LAML) is not entirely understood. Methods We analyzed the expression of HDAC3 in normal and cancerous tissues using Oncomine, UALCAN, and GEO databases. Changes of the HDAC3 gene were analyzed by cBioPortal. The genes coexpressed with HDAC3 were analyzed by WebGestalt, and the predicted signaling pathways in KEGG were discussed. Results We discovered that the expression of HDAC3 was elevated in some types of acute myeloid leukemia. The HDAC3 gene has a strong positive correlation with SLC25A5, NDUFA2, Cox4I1, and EIF3K, which regulate cell growth and development. HDAC3 transcription is higher in patients with FLT3 mutation than in healthy people. HDAC3 can be directly involved in regulating the thyroid hormone signaling pathway. MEF2D is directly involved in the cGMP-PKG signaling pathway, and the HDAC3 gene has a strong synergistic relationship with MEF2D. HDAC3 is indirectly involved in the cGMP-PKG signaling pathway, thereby indirectly regulating the expression levels of p53 and p21 genes in patients with LAML. Genomics of Drug Sensitivity in Cancer (GDSC) database analysis revealed that the application of the HDAC3 inhibitor can inhibit the proliferation of leukemia cells. Conclusions Therefore, our data suggest that HDAC3 may be a possible therapeutic target for acute myeloid leukemia.

The N6-Methylandenosine-Related Gene BIRC5 as a Prognostic Biomarker Correlated With Cell Migration and Immune Cell Infiltrates in Low Grade Glioma.

Gliomas account for 75% of all primary malignant brain tumors in adults and are associated with high mortality. Emerging evidence has demonstrated that baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) plays a critical role in cell apoptosis and the progression of diverse cancers. However, no studies have yet focused on the immunological function and mechanisms of upstream BIRC5 regulation in the progression of low-grade gliomas (LGG). Here, we evaluated BIRC5 expression and clinical characteristics in people with LGG using the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, Gene Expression Omnibus, Rembrandt, and Gravendeel databases. We used Kaplan–Meier statistics and receiver operating characteristic (ROC) curves to analyze the prognostic value of BIRC5 in LGG. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment terms were also explored to identify functional roles of BIRC5. The Tumor Immune Estimation Resource (TIMER) and Tumor Immune System Interaction (TISIDB) databases were used to examine the correlation between BIRC5 expression and immune cell infiltration in LGG. The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) databases were used to examine the potential drugs targeting BIRC5. We used transwell and wound healing assays to determine the biological functions of BIRC5 in glioma cell migration. Our results demonstrated that BIRC5 was highly expressed in LGG and the expression level correlated with tumor grade, prognosis, histological subtype, isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q chromosomal co-deletion, chemotherapy status, and O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. GO and KEGG analysis showed that BIRC5 is primarily involved in cell proliferation and immune response-related signaling pathways. We also found that BIRC5 was significantly correlated with m6A modification and diverse drug sensitivity. TIMER and TISIDB database analysis showed that BIRC5 expression is associated with infiltration of diverse immune cells and immune modulation in LGG. BIRC5 knockdown inhibited LGG cell migration. Collectively, our results demonstrate that BIRC5 is correlated with cell migration and immune infiltration in LGG and may be a useful prognostic biomarker.

Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution.

Different cancer patients may respond differently to cancer treatment due to the heterogeneity of cancer. It is an urgent task to develop an efficient computational method to identify drug responses in different cell lines, which guides us to design personalized therapy for an individual patient. Hence, we propose an end-to-end algorithm, namely MOFGCN, to predict drug response in cell lines based on Multi-Omics Fusion and Graph Convolution Network. MOFGCN first fuses multiple omics data to calculate the cell line similarity and then constructs a heterogeneous network by combining the cell line similarity, drug similarity, and the known cell line-drug associations. Secondly, it learns the latent features for cancer cell lines and drugs by performing graph convolution operations on the heterogeneous network. Finally, MOFGCN applies the linear correlation coefficient to reconstruct the cancer cell line-drug correlation matrix to predict drug sensitivity. To our knowledge, this is the first attempt to combine graph convolutional neural network and linear correlation coefficient for this significant task. We performed extensive evaluation experiments on the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) databases to validate MOFGCN's performance. The experimental results show that MOFGCN is superior to the state-of-the-art algorithms in predicting missing drug responses. It also leads to higher performance in predicting drug responses for new cell lines, new drugs, and targeted drugs.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer.

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

Integratively Genomic Analysis Reveals the Prognostic and Immunological Characteristics of Pyroptosis and Ferroptosis in Pancreatic Cancer for Precision Immunotherapy.

The non-apoptotic cell death processes including pyroptosis and ferroptosis have been implicated in the progression and therapeutic responses of pancreatic adenocarcinoma (PAAD). However, the extent to which pyroptosis and ferroptosis influence tumor biology remains ambiguous, especially in PAAD, which is characterized with “cold” immunity. Considering the heterogeneity among different patients, it was more practical to quantify distinct cell death profiles in an individual tumor sample. Herein, we developed a pyroptosis-ferroptosis (P-F) score for PAAD patients in The Cancer Genome Atlas (TCGA) database. A high P-F score was associated with active immune phenotype, decreased genomic alterations, and significantly longer survival. Good accuracy of the P-F score in predicting overall survival (OS) was further confirmed in the TCGA-PAAD, ICGC-PACA-CA, and E-MTAB-6134 cohorts. Besides, one immunotherapy cohort (IMvigor210 dataset) has verified that patients with high P-F scores exhibited significant advantages in therapeutic responses and clinical benefits. The sensitivity to chemotherapeutics was analyzed through the Genomics of Drug Sensitivity in Cancer (GDSC), and patients with low P-F score might be more sensitive to paclitaxel and 5-fluorouracil. Collectively, the P-F score based on the systematic evaluation of cell death profiles could serve as an effective biomarker in predicting the outcomes and responses of PAAD patients to treatments with chemotherapeutic agents or immunotherapies.

Pan-Cancer Analysis Reveals Genomic and Clinical Characteristics of TRPV Channel-Related Genes.

BackgroundTransient Receptor Potential channels (TRPs), a class of ion channels, were first described two decades ago. Many TRP family members are major participants in nociception and integration of heat and pain signals. Recent studies have revealed that subfamilies of this channel, such as members of transient receptor potential vanilloid (TRPV) channels, play important roles in breast, ovarian, prostate, and pancreatic cancers.MethodsWe performed a comprehensive analysis of TRPVs in 9125 tumor samples of 33 cancer types using multi-omics data extracted from The Cancer Genome Atlas (TCGA). We identified differences in mRNA expression in a pan-cancer analysis, and the genomic characteristics of single nucleotide variations, copy number variations, methylation features, and miRNA–mRNA interactions using data from TCGA. Finally, we evaluated the sensitivity and resistance to drugs targeting TRPV channel-related genes using the Cancer Therapeutics Response Portal (CTRP) and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Finally, we validated the drug sensitive data and the importance of TRPV6 in two cancer cell lines using q-PCR assay, CCK8 assay, EdU assay and scratch assay.ResultsExtensive genetic alterations in TRPV channel-related genes and differences in gene expression were associated with the activity of cancer marker-related pathways. TRPV channel-related genes can be used as prognostic biomarkers. Several potential drugs, such as lapatinib, that may target TRPV channel-related genes were identified by mining the genomics of drug sensitivity.ConclusionThis study revealed the genomic changes and clinical characteristics of TRPV channel-related regulatory factors in 33 types of tumors. This analysis may help uncover the TRPV channel-related genes associated with tumorigenesis. We also proposed novel strategies for tumor treatment.

SDC: An integrated database for sex differences in cancer

Sex differences are evident in the incidence and mortality of diverse cancers. With the development of personalized approaches in cancer treatment, the impact of sex differences has not been systematically incorporated into preclinical and clinical cancer research. The molecular mechanisms underlying sex differences in cancer have not been elucidated. Here, we developed the first database of Sex Differences in Cancer (SDC), a web-based public database that integrates resources from multiple databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UCSC Xena, Broad Institute Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC). SDC contains 27 types of cancers, 6 types of molecular data, more than 10,000 donors, 977 cancer cell lines were used to analyze sex differences among cancers. It provides five main modules: Survival and phenotype, Molecular differences, Signatures and pathways, Therapy response, Download. Users can download the all the visualized results and raw data after analysis. Collectively, SDC is the first integrated database to analyze sex differences in cancer on the web server, which will strengthen our understanding of the role of sex in cancers. It is implemented in Shiny-server and freely available for public use at http://sdc.anticancer.xyz.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

BackgroundPharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). MethodsA total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. ResultsThree reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. ConclusionWe provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

MUC16 Alternation is a Potential Biomarker for Prognosis in Patients with Gastric Cancer Receiving Immunotherapy

Background: Immune checkpoint inhibitors (ICIs) have recently attracted much attention for the treatment of gastric cancer. However, the response rate of patients without selective treatment is low. Therefore, screening for the predictors of ICIs efficacy prior to treatment is the key to providing the ideal treatment. Cell surface associated Mucin 16 (MUC16) is significantly related to the occurrence and progress of various malignant tumors. Therefore, the relationship between MUC16 and the efficacy of ICIs, as well as its potential mechanism, require further study. Methods: We began by collecting a gastric cancer data set from a public database, and the immunotherapy cohort that we chose was selected from published studies. We analyzed the influence of MUC16 mutation on clinical prognosis, gene mutation frequency, and the immune microenvironment. Gene Set Enrichment Analysis (GSEA) was used to evaluate significant up-regulation and down-regulation in response pathways. We used the drug data from the Genomics of Drug Sensitivity in Cancer (GDSC) database to analyze the influence of MUC16 mutation on drug sensitivity. Results: Univariate and multivariate Cox regression analysis and logistic regression analysis show that the presence of MUC16 mutation can result in a better prognosis for patients and high efficacy of immunotherapy, and as such can be used as an independent factor to predict the efficacy of ICIs. The analysis results of immunogenicity and the immune microenvironment showed that MUC16 mutation causes a higher tumor mutation load and new antigen load, higher levels of activated immune cells, and significantly up-regulated immune response-related pathways. Conclusions: MUC16 mutation can be used as an independent predictor of the curative effect of ICIs treatment in patients with gastric cancer, and it is related to greater immunotherapy efficacy, higher immunogenicity, and a significantly improved immune microenvironment.

Identification of a Novel PPAR Signature for Predicting Prognosis, Immune Microenvironment, and Chemotherapy Response in Bladder Cancer.

Background Mounting evidence has confirmed that peroxisome proliferator-activated receptors (PPARs) played a crucial role in the development and progression of bladder cancer (BLCA). The purpose of this study is to comprehensively investigate the function and prognostic value of PPAR-targeted genes in BLCA. Methods The RNA sequencing data and clinical information of BLCA patients were acquired from The Cancer Genome Atlas (TCGA). The differentially expressed PPAR-targeted genes were investigated. Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis were performed for screening prognostic PPAR-targeted genes and constructing the prognostic PPAR signature and then validated by GSE13507 cohort and GSE32894 cohort. A nomogram was constructed to predict the outcomes of BLCA patients in combination with PPAR signature and clinical factors. Gene set enrichment analysis (GSEA) and immune cell infiltration were implemented to explore the molecular characteristics of the signature. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to predict the chemotherapy responses of the prognostic signature. The candidate small molecule drugs targeting PPAR-targeted genes were screened by the CMAP database. Results We constructed and validated the prognostic signature comprising of 4 PPAR-targeted genes (CPT1B, CALR, AHNAK, and FADS2), which was an independent prognostic biomarker in BLCA patients. A nomogram based on the signature and clinical factors was established in the TCGA set, and the calibration plots displayed the excellent predictive capacity. GSEA analysis indicated that PPAR signature was implicated in multiple oncogenic signaling pathways and correlated with tumor immune cell infiltration. Patients in the high-risk groups showed greater sensitivity to chemotherapy than those in the low-risk groups. Moreover, 11 candidate small molecule drugs were identified for the treatment of BLCA. Conclusion We constructed and validated a novel PPAR signature, which showed the excellent performance in predicting prognosis and chemotherapy sensitivity of BLCA patients.

The identification of lung cancer gene-drug module based on multiplex networks algorithm

Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer.

Background: The unfolded protein response (UPR) plays a significant role in maintaining protein hemostasis in tumor cells, which are crucial for tumor growth, invasion, and resistance to therapy. This study aimed to develop a UPR-related signature and explore its correlation with immunotherapy and chemotherapy in bladder cancer. Methods: The differentially expressed UPR-related genes were put into Lasso regression to screen out prognostic genes, which constituted the UPR signature, and were incorporated into multivariate Cox regression to generate risk scores. Subsequently, the predictive performance of this signature was estimated by receiver operating characteristic (ROC) curves. The CIBERSORTx, the maftool, and Gene set enrichment analysis (GSEA) were applied to explore infiltrated immune cells, tumor mutational burden (TMB), and enriched signaling pathways in both risk groups, respectively. Moreover, The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases were used to predict responses to chemotherapy and immunotherapy. Results: Twelve genes constituted the UPR-related signature. Patients with higher risk scores had worse overall survival (OS) in training and three validation sets. The UPR-related signature was closely correlated with clinicopathologic parameters and could serve as an independent prognostic factor. M0 macrophages showed a significantly infiltrated difference in both risk groups. TMB analysis showed that the risk score in the wild type and mutation type of FGFR3 was significantly different. GSEA indicated that the immune-, extracellular matrix-, replication and repair associated pathways belonged to the high risk group and metabolism-related signal pathways were enriched in the low risk group. Prediction of immunotherapy and chemotherapy revealed that patients in the high risk group might benefit from chemotherapy, but had a worse response to immunotherapy. Finally, we constructed a predictive model with age, stage, and UPR-related risk score, which had a robustly predictive performance and was validated in GEO datasets. Conclusion: We successfully constructed and validated a novel UPR-related signature in bladder cancer, which could robustly predict survival outcomes and closely correlate with the response to immunotherapy and chemotherapy in bladder cancer.

Multi-Omics Reveal the Immunological Role and the Theragnostic Value of miR-216a/GDF15 Axis in Human Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is the most common type of gastrointestinal cancer and is still the third leading cause of cancer-related mortality worldwide. Accurate screening tools for early diagnosis and prediction of prognosis and precision treatment strategies are urgently required to accommodate the unmet medical needs of COAD management. We herein aimed to explore the significance of the microRNA (miR)-216a/growth differentiation factor 15 (GDF15) axis in terms of clinical value, tumor immunity, and potential mechanisms in COAD by using multi-omic analysis. The gene expression levels of miR-216a and GDF15 showed an increase in the COAD group compared to those of the normal group. The expression of miR-216a presented a negative correlation with GDF15 in COAD tumor tissue. The use of an in vitro luciferase reporter assay and bioinformatic prediction revealed that miR-216a-3p acted toward translational inhibition on GDF15 by targeting its 3′untranslated region (UTR) site. High miR-216a expression was associated with decreased overall survival (OS), while the high expression of GDF15 was associated with increased OS. Enriched type 1 T-helper (Th1), enriched regulatory T (Treg), enriched eosinophils, and decreased nature killer T-cells (NKTs) in COAD tumor tissue may play counteracting factors on the tumor-regulatory effects of miR-216a and GDF15. In addition, high GDF15 expression had associations with suppressed immunoinhibitory genes and negative correlations with the infiltration of macrophages and endothelial cells. The enrichment analysis revealed that GDF15 and its co-expression network may be implicated in mitochondrial organization, apoptosis signaling, and endoplasmic reticulum (ER) stress response. The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) analysis identified that Gemcitabine acted as a precision treatment for COAD when GDF15 expression was low. This study supports the miR-216a/GDF15 axis as a diagnostic/prognostic panel for COAD, identifies Th1, Treg, eosinophils, and NKTs as counteracting factors, indicates potential relationships underlying immunomodulation, mitochondrial organization, apoptotic signaling, and ER stress and unveil Gemcitabine as a potential drug for the development of treatment strategy when combined with targeting GDF15.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy.

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored. Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs. Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes. Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Novel Biomarker Genes for Prognosis of Survival and Treatment of Glioma.

Glioblastoma multiforme (GBM) is the most aggressive malignant primary central nervous system tumor. Although surgery, radiotherapy, and chemotherapy treatments are available, the 5-year survival rate of GBM is only 5.8%. Therefore, it is imperative to find novel biomarker for the prognosis and treatment of GBM. In this study, a total of 141 differentially expressed genes (DEGs) in GBM were identified by analyzing the GSE12657, GSE90886, and GSE90598 datasets. After reducing the data dimensionality, Kaplan-Meier survival analysis indicated that expression of PTPRN and RIM-BP2 were downregulated in GBM tissues when compared with that of normal tissues and that the expression of these genes was a good prognostic biomarker for GBM (p<0.05). Then, the GSE46531 dataset and the Genomics of Drug Sensitivity in Cancer (GDSC) database were used to examine the relationship between sensitivity radiotherapy (RT) and chemotherapy for GBM and expression of PTPRN and RIM-BP2. The expression of PTPRN was significantly high in RT-resistant patients (p<0.05) but it was not related to temozolomide (TMZ) resistance. The expression level of RIM-BP2 was not associated with RT or TMZ treatment. Among the chemotherapeutic drugs, cisplatin and erlotinib had a significantly good treatment effect for glioma with expression of PTPRN or RIM-BP2 and in lower-grade glioma (LGG) with IDH mutation. (p < 0.05). The tumor mutational burden (TMB) score in the low PTPRN expression group was significantly higher than that in the high PTPRN expression group (p=0.013), with a large degree of tumor immune cell infiltration. In conclusion, these findings contributed to the discovery process of potential biomarkers and therapeutic targets for glioma patients.

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

Attenuated expression of SNF5 facilitates progression of bladder cancer via STAT3 activation

BackgroundSWI/SNF, a well-known ATP-dependent chromatin-remodeling complex, plays an essential role in several biological processes. SNF5, the core subunit of the SWI/SNF remodeling complex, inactivated in 95% of malignant rhabdoid tumors (MRT), highlighting its significance in tumorigenesis. However, the role of SNF5 in bladder cancer (BC) remains unknown. In this study, we aimed to investigate the function and potential clinical applicability of SNF5 in BC.MethodsData from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Cancer Cell Line Encyclopedia (CCLE) databases were used to evaluate the clinical significance of SNF5 in BC. We performed Gene Set Enrichment Analysis (GSEA) and functional assays to investigate the role of SNF5 in BC. Genomics of Drug Sensitivity in Cancer (GDSC) and drug-susceptibility tests were performed to identify the potential value of SNF5 in the treatment of BC.ResultsLow SNF5 expression conferred a poor prognosis and was significantly associated with the N-stage in BC. ROC curves indicated that SNF5 could distinguish BC from the normal tissues. In vitro and in vivo functional assays demonstrated that attenuated SNF5 expression could promote cell proliferation and enhance migration by STAT3 activation. We imputed that low SNF5 expression could confer greater resistance against conventional first-line drugs, including cisplatin and gemcitabine in BC. GDSC and drug-resistance assays suggested that low SNF5 expression renders T24 and 5637 cells high sensitivity to EGFR inhibitor gefitinib, and combination of EZH2 inhibitor GSK126 and cisplatin.ConclusionsTo the best of our knowledge, the present study, for the first time, showed that low SNF5 expression could promote cell proliferation and migration by activating STAT3 and confer poor prognosis in BC. Importantly, SNF5 expression may be a promising candidate for identifying BC patients who could benefit from EGFR-targeted chemotherapy or cisplatin in combination with EZH2 inhibitor treatment regimens.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models.

Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer.

BackgroundRecently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration.MethodThe fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs.ResultsFive hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC.ConclusionThis study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

Downregulation of the Coiled-Coil Domain Containing 80 and Its Perspective Mechanisms in Ovarian Carcinoma: A Comprehensive Study.

Introduction We aimed to explore the downregulation of the coiled-coil domain containing 80 (CCDC80) and its underlying molecular mechanisms in ovarian carcinoma (OVCA). Materials/Methods. Immunohistochemical staining was performed to confirm the expression status of CCDC80 protein. Combining the data from in-house tissue microarrays and high-throughput datasets, we identified the expression level of CCDC80 in OVCA. We utilized cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between CCDC80 and the tumor microenvironment (TME) landscape in OVCA. Pathway enrichment, function annotation, and transcription factor (TFs) exploration were conducted to study the latent molecular mechanisms. Moreover, the cell line data in the Genomics of Drug Sensitivity in Cancer (GDSC) database was used to discover the relationship between CCDC80 and drug sensitivity. Results An integrated standard mean difference (SMD) of −0.919 (95% CI: −1.515–0.324, P = 0.002) identified the downregulation of CCDC80 in OVCA based on 1048 samples, and the sROC (AUC = 0.76) showed a moderate discriminatory ability of CCDC80 in OVCA. The fraction of infiltrating naive B cells showed significant differences between the high- and low-CCDC80 expression groups. Also, CCDC80-related genes are enriched in the Ras signaling pathway and metabolic of lipid. Nuclear receptor subfamily three group C member 1 (NR3C1) may be an upstream TF of CCDC80, and CCDC80 may be related to the sensitivity of mitocycin C and nilotinib. Conclusion CCDC80 was downregulated in OVCA and may play a role as a tumor suppressor in OVCA.