Drug Sensitivity Research Articles

To explore the mechanism of Yigong San anti-gastric cancer and immune regulation

BACKGROUND Yigong San (YGS) is a representative prescription for the treatment of digestive disorders, which has been used in clinic for more than 1000 years. However, the mechanism of its anti-gastric cancer and regulate immunity are still remains unclear. AIM To explore the mechanism of YGS anti-gastric cancer and immune regulation. METHODS Firstly, collect the active ingredients and targets of YGS, and the differentially expressed genes of gastric cancer. Secondly, constructed a protein-protein interaction network between the targets of drugs and diseases, and screened hub genes. Then the clinical relevance, mutation and repair, tumor microenvironment and drug sensitivity of the hub gene were analyzed. Finally, molecular docking was used to verify the binding ability of YGS active ingredient and hub genes. RESULTS Firstly, obtained 55 common targets of gastric cancer and YGS. The Kyoto Encyclopedia of Genes and Genomes screened the microtubule-associated protein kinase signaling axis as the key pathway and IL6, EGFR, MMP2, MMP9 and TGFB1 as the hub genes. The 5 hub genes were involved in gastric carcinogenesis, staging, typing and prognosis, and their mutations promote gastric cancer progression. Finally, molecular docking results confirmed that the components of YGS can effectively bind to therapeutic targets. CONCLUSION YGS has the effect of anti-gastric cancer and immune regulation.

Four centrosome-related genes to predict the prognosis and drug sensitivity of patients with colon cancer

BACKGROUND As the primary microtubule organizing center in animal cells, centrosome abnormalities are involved in human colon cancer. AIM To explore the role of centrosome-related genes (CRGs) in colon cancer. METHODS CRGs were collected from public databases. Consensus clustering analysis was performed to separate the Cancer Genome Atlas cohort. Univariate Cox and least absolute shrinkage selection operator regression analyses were performed to identify candidate prognostic CRGs and construct a centrosome-related signature (CRS) to score colon cancer patients. A nomogram was developed to evaluate the CRS risk in colon cancer patients. An integrated bioinformatics analysis was conducted to explore the correlation between the CRS and tumor immune microenvironment and response to immunotherapy, chemotherapy, and targeted therapy. Single-cell transcriptome analysis was conducted to examine the immune cell landscape of core prognostic genes. RESULTS A total of 726 CRGs were collected from public databases. A CRS was constructed, which consisted of the following four genes: TSC1 , AXIN2 , COPS7A , and MTUS1 . Colon cancer patients with a high-risk signature had poor survival. Patients with a high-risk signature exhibited decreased levels of plasma cells and activated memory CD4+ T cells. Regarding treatment response, patients with a high-risk signature were resistant to immunotherapy, chemotherapy, and targeted therapy. COPS7A expression was relatively high in endothelial cells and fibroblasts. MTUS1 expression was high in endothelial cells, fibroblasts, and malignant cells. CONCLUSION We constructed a centrosome-related prognostic signature that can accurately predict the prognosis of colon cancer patients, contributing to the development of individualized treatment for colon cancer.

Prognostic value and drug sensitivity of F‑box and leucine‑rich repeat protein 6 in glioma

Prognostic value and drug sensitivity of F‑box and leucine‑rich repeat protein 6 in glioma

Carbon nanotubes conjugated with cisplatin activate different apoptosis signaling pathways in 2D and 3D-spheroid triple-negative breast cancer cell cultures: a comparative study.

The type of experimental model for the in vitro testing of drug formulations efficiency represents an important tool in cancer biology, with great attention being granted to three-dimensional (3D) cultures as these offer a closer approximation of the clinical sensitivity of drugs. In this study, the effects induced by carboxyl-functionalized single-walled carbon nanotubes complexed with cisplatin (SWCNT-COOH-CDDP) and free components (SWCNT-COOH and CDDP) were compared between conventional 2D- and 3D-spheroid cultures of human breast cancer cells. The 2D and 3D breast cancer cultures were exposed to various doses of SWCNT-COOH (0.25-2μg/mL), CDDP (0.158-1.26μg/mL) and the same doses of SWNCT-COOH-CDDP complex for 24 and 48h. The anti-tumor activity, including modulation of cell viability, oxidative stress, proliferation, apoptosis, and invasion potential, was explored by spectrophotometric and fluorometric methods, immunoblotting, optical and fluorescence microscopy. The SWCNT-COOH-CDDP complex proved to have high anti-cancer efficiency on 2D and 3D cultures by inhibiting cell proliferation and activating cell death. A dose of 0.632μg/mL complex triggered different pathways of apoptosis in 2D and 3D cultures, by intrinsic, extrinsic, and reticulum endoplasmic pathways. Overall, the 2D cultures showed higher susceptibility to the action of complex compared to 3D cultures and SWCNT-COOH-CDDP proved enhanced anti-tumoral activity compared to free CDDP.

Identification of a fatty acid metabolism-related gene signature to predict prognosis in stomach adenocarcinoma.

Fatty acid metabolism (FAM) contributes to tumorigenesis and tumor development, but the role of FAM in the progression of stomach adenocarcinoma (STAD) has not been comprehensively clarified. The expression data and clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA). FAM pathway was analyzed by gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) methods. Univariate Cox regression analysis was conducted to select prognosis genes. Molecular subtypes were classified by consensus clustering analysis. Furthermore, least absolute shrinkage and selection operator (Lasso) analysis was employed to develop a risk model. ESTIMATE and tumour immune dysfunction and exclusion (TIDE) algorithm were used to assess immunity. pRRophetic package was conducted to predict drug sensitivity. Based on 14 FAM related prognosis genes (FAMRG), 2 clusters were determined. Patients in C2 showed a worse overall survival (OS). Furthermore, a 7-FAMRG risk model was established as an independent predictor for STAD, with a higher riskscore indicating an unfavorable OS. High riskscore patients had higher TIDE score and these patients were more sensitive to anticancer drugs such as Bortezomib, Dasatinib and Pazopanib. A nomogram based on riskscore was an effective prediction tool applicable to clinical settings. The results from pan-cancer analysis supported a prominent application value of riskscore model in other cancer types. The FAMRGs model established in this study could help predict STAD prognosis and offer new directions for future studies on dysfunctional FAM-induced damage and anti-tumor drugs in STAD disease.

Characterization of alternative splicing events and prognostic signatures in gastric cancer

BackgroundAccumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.MethodsThe expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted.ResultsIn total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups.ConclusionsIn this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.

Activated interferon response from DNA damage in multiple myeloma cells contributes to the chemotherapeutic effects of anthracyclines

IntroductionMultiple myeloma (MM) is a malignant plasma cell disease caused by abnormal proliferation of clonal plasma cells in bone marrow. Upfront identification of tumor subgroups with specific biological markers has the potential to improve biologically-driven therapy. Previously, we established a molecular classification by stratifying multiple myeloma into two subtypes with a different prognosis based on a gene module co-expressed with MCL-1 (MCL1-M).MethodsGene Ontology (GO) analysis with differentially expressed genes was performed to identify signal pathway. Drug sensitivity was analyzed using the OncoPredict algorithm. Drug sensitivity of different myeloma cell lines was detected by CCK8 and flow cytometry. RNA-seq was performed on drug-sensitive cell lines before and after adriamycin treatment. RT-qPCR was used to further verify the sequencing results. The expression of γ-H2AX and dsDNA in sensitive and resistant cell lines was detected by immunofluorescence method.ResultsIn our study, we demonstrated that MCL1-M low MM were more sensitive to anthracyclines. We treated different myeloma cell lines with doxorubicin in vitro and discovered the association of drug sensitivity with IFN signaling. Herein, we demonstrate that the doxorubicin-sensitive myeloma cell line showed significant DNA damage and up-regulated expression of genes related to the IFN response, which was not observed in drug-insensitive cell lines.DiscussionOur results suggest that the active IFN signaling pathway may serve as a marker for predicting chemotherapy sensitivity in patients with myeloma. With our MCL1-M molecular classification system, we can screen patients with a potentially good response to the interferon signaling pathway and provide individualized treatment for MM. We propose IFN-a as adjuvant therapy for patients with myeloma sensitive to anthracyclines to further improve the therapeutic effect and prolong the survival of patients.

Elucidating the impact of parthanatos-related microRNAs on the tumoral immune microenvironment and clinical outcome in low-grade gliomas

Parthanatos, a cell death mechanism triggered by PARP-1 activation, is implicated in oncogenic processes, yet their role in low-grade gliomas (LGG) remains poorly understood. This research investigates Parthanatos-related miRNAs' prognostic and immunomodulatory potential, alongside their influence on therapeutic outcomes in LGGs. Comprehensive miRNA and mRNA profiles of LGG patients were extracted from TCGA and CGGA databases, integrating clinical parameters to identify Parthanatos-associated miRNAs. IHC data validated the expression levels of Parthanatos-related genes in glioma versus normal brain tissues. Protein–protein interaction networks and Spearman correlation analysis facilitated the identification of key miRNAs. Parthanatos-related miRNA indices (PMI) were screened using Lasso and assessed for their accuracy in predicting prognosis, comparing their associated potential molecular functions and heterogeneity of the immune microenvironment. Drug sensitivity was assessed between different groups and optimal therapeutic agents were predicted. Validate the expression levels of key miRNAs by qPCR. Ninety-one miRNAs significantly associated with Parthanatos were screened, through which a PMI prognosis model of nine miRNAs was constructed. The PMI score was able to independently predict the prognosis of patients with LGG, and the nomogram constructed based on the PMI provided a practical tool for clinical prediction of patient prognosis. The proportion of immune response was lower in patients in the high-risk group, and there were significant differences in drug sensitivity between different risk classes, while drugs such as Fasudil were identified as the most promising therapeutic agents for patients in the high-risk group. Our findings highlight the critical role of Parthanatos-associated miRNAs in the progression and treatment of LGG, offering novel insights into their prognostic value and therapeutic potential.

Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing.

Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

Lysosome-related genes predict acute myeloid leukemia prognosis and response to immunotherapy

BackgroundAcute myeloid leukemia (AML) is a highly aggressive and pathogenic hematologic malignancy with consistently high mortality. Lysosomes are organelles involved in cell growth and metabolism that fuse to form specialized Auer rods in AML, and their role in AML has not been elucidated. This study aimed to identify AML subtypes centered on lysosome-related genes and to construct a prognostic model to guide individualized treatment of AML.MethodsGene expression data and clinical data from AML patients were downloaded from two high-throughput sequencing platforms. The 191 lysosomal signature genes were obtained from the database MsigDB. Lysosomal clusters were identified by unsupervised consensus clustering. The differences in molecular expression, biological processes, and the immune microenvironment among lysosomal clusters were subsequently analyzed. Based on the molecular expression differences between lysosomal clusters, lysosomal-related genes affecting AML prognosis were screened by univariate cox regression and multivariate cox regression analyses. Algorithms for LASSO regression analyses were employed to construct prognostic models. The risk factor distribution, KM survival curve, was applied to evaluate the survival distribution of the model. Time-dependent ROC curves, nomograms and calibration curves were used to evaluate the predictive performance of the prognostic models. TIDE scores and drug sensitivity analyses were used to explore the implication of the model for AML treatment.ResultsOur study identified two lysosomal clusters, cluster1 has longer survival time and stronger immune infiltration compared to cluster2. The differences in biological processes between the two lysosomal clusters are mainly manifested in the lysosomes, vesicles, immune cell function, and apoptosis. The prognostic model consisting of six prognosis-related genes was constructed. The prognostic model showed good predictive performance in all three data sets. Patients in the low-risk group survived significantly longer than those in the high-risk group and had higher immune infiltration and stronger response to immunotherapy. Patients in the high-risk group showed greater sensitivity to cytarabine, imatinib, and bortezomib, but lower sensitivity to ATRA compared to low -risk patients.ConclusionOur prognostic model based on lysosome-related genes can effectively predict the prognosis of AML patients and provide reference evidence for individualized immunotherapy and pharmacological chemotherapy for AML.

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

BackgroundThe 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.ResultsThis 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.ConclusionsOur 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).

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

The analysis of transcriptomic signature of TNBC—searching for the potential RNA-based predictive biomarkers to determine the chemotherapy sensitivity

AbstractNeoadjuvant chemotherapy is the foundation treatment for triple-negative breast cancer (TNBC) and frequently results in pathological complete response (pCR). However, there are large differences in clinical response and survival after neoadjuvant chemotherapy of TNBC patients. The aim was to identify genes whose expression significantly associates with the efficacy of neoadjuvant chemotherapy in patients with TNBC. Transcriptomes of 46 formalin-fixed paraffin-embedded (FFPE) tumor samples from TNBC patients were analyzed by RNA-seq by comparing 26 TNBCs with pCR versus 20 TNBCs with pathological partial remission (pPR). Subsequently, we narrowed down the list of genes to those that strongly correlated with drug sensitivity of 63 breast cancer cell lines based on Dependency Map Consortium data re-analysis. Furthermore, the list of genes was limited to those presenting specific expression in breast tumor cells as revealed in three large published single-cell RNA-seq breast cancer datasets. Finally, we analyzed which of the selected genes were significantly associated with overall survival (OS) in TNBC TCGA dataset. A total of 105 genes were significantly differentially expressed in comparison between pPR versus pCR. As revealed by PLSR analysis in breast cancer cell lines, out of 105 deregulated genes, 42 were associated with sensitivity to docetaxel, doxorubicin, paclitaxel, and/or cyclophosphamide. We found that 24 out of 42 sensitivity-associated genes displayed intermediate or strong expression in breast malignant cells using single-cell RNAseq re-analysis. Finally, 10 out of 24 genes were significantly associated with overall survival in TNBC TCGA dataset. Our RNA-seq-based findings suggest that there might be transcriptomic signature consisted of 24 genes specifically expressed in tumor malignant cells for predicting neoadjuvant response in FFPE samples from TNBC patients prior to treatment initiation. Additionally, nine out of 24 genes were potential survival predictors in TNBC. This group of 24 genes should be further investigated for its potential to be translated into a predictive test(s).

Confronting Tuberculosis: A Synthetic Quinoline-Isonicotinic Acid Hydrazide Hybrid Compound as a Potent Lead Molecule Against Mycobacterium tuberculosis.

The current tuberculosis (TB) treatment is challenged by a complex first-line treatment for drug-sensitive (DS) TB. Additionally, the prevalence of multidrug (MDR)- and extensively drug (XDR)-resistant TB necessitates the search for new drug prototypes. We synthesized and screened 30 hybrid compounds containing aminopyridine and 2-chloro-3-formyl quinoline to arrive at a compound with potent antimycobacterial activity, UH-NIP-16. Subsequently, antimycobacterial activity against DS and MDR Mycobacterium tuberculosis (M.tb) strains were performed. It demonstrated an MIC50 value of 1.86 ± 0.21 μM for laboratory pathogenic M.tb strain H37Rv and 3.045 ± 0.813 μM for a clinical M.tb strain CDC1551. UH-NIP-16 also decreased the MIC50 values of streptomycin, isoniazid, ethambutol, and bedaquiline to about 45, 55, 68, and 76%, respectively, when used in combination, potentiating their activities. The molecule was active against a clinical MDR M.tb strain. Cytotoxicity on PBMCs from healthy donors and on human cell lines was found to be negligible. Further, blind docking of UH-NIP-16 using Auto Dock Vina and MGL tools onto diverse M.tb proteins showed high binding affinities with multiple M.tb proteins, the top five targets being metabolically critical proteins CelA1, DevS, MmaA4, lysine acetyltransferase, and immunity factor for tuberculosis necrotizing toxin. These bindings were confirmed by fluorescence spectroscopy using a representative protein, MmaA4. Envisaging that a pathogen will have a lower probability of developing resistance to a hybrid molecule with multiple targets, we propose that UH-NIP-16 can be further developed as a lead molecule with the bacteriostatic potential against M.tb, both alone and in combination with first-line drugs.

Constructing a novel prognostic model for triple-negative breast cancer based on genes associated with vasculogenic mimicry.

Research has shown a connection between vasculogenic mimicry (VM) and cancer progression. However, the functions of genes related to VM in the emergence and progression of TNBC have not been completely elucidated. A survival risk model was constructed by screening biomarkers using DESeq2 and WGCNA based on public TNBC transcriptome data. Furthermore, gene set enrichment analysis was performed, and tumor microenvironment and drug sensitivity were analyzed. The selected biomarkers were validated via quantitative PCR detection, immunohistochemical staining, and protein detection in breast cancer cell lines. Biomarkers related to the proliferation and migration of TNBC cells were validated via in vitro experiments. The findings revealed that 235 target genes were connected to the complement and coagulation cascade pathways. The risk score was constructed using KCND2, NRP1, and VSTM4. The prognosis model using the risk score and pathological T stage yielded good validation results. The clinical risk of TNBC was associated with the angiogenesis signaling pathway, and the low-risk group exhibited better sensitivity to immunotherapy. Quantitative PCR and immunohistochemistry indicated that the expression levels of KCND2 in TNBC tissues were higher than those in adjacent nontumor tissues. In the TNBC cell line, the protein expression of KCND2 was increased. Knockdown of KCND2 and VSTM4 inhibited the proliferation and migration of TNBC cells in vitro. In this study, three VM-related biomarkers were identified, including KCND2, NRP1, and VSTM4. These findings are likely to aid in deepening our understanding of the regulatory mechanism of VM in TNBC.

Isocucurbitacin B inhibits glioma growth through PI3K/AKT pathways and increases glioma sensitivity to TMZ by inhibiting hsa-mir-1286a

Aim: Glioma accounts for 81% of all cancers of the nervous system cancers and presents one of the most drug-resistant malignancies, resulting in a relatively high mortality rate. Despite extensive efforts, the complete treatment options for glioma remain elusive. The effect of isocucurbitacin B (isocuB), a natural compound extracted from melon pedicels, on glioma has not been investigated. This study aims to investigate the inhibitory effect of isocuB on glioma and elucidate its underlying mechanisms, with the objective of developing it as a potential therapeutic agent for glioma. Methods: We used network pharmacology and bioinformatics analysis to predict potential targets and associated pathways of isocuB in glioma. Subsequently, the inhibitory effect of isocuB on glioma and its related mechanisms were assessed through Counting Kit-8 (CCK-8), wound healing, transwell, Western blot (WB), reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and other in vitro experiments, alongside tumor formation experiments in nude mice. Results: Based on this investigation, it suggested that isocuB might inhibit the growth of gliomas through the PI3K-AKT and MAPK pathways. Additionally, we proposed that isocuB may enhance glioma drug sensitivity to temozolomide (TMZ) via modulation of hsa-mir-1286a. The CCK-8 assay revealed that isocuB exhibited inhibitory effects on U251 and U87 proliferation and outperformed TMZ. Wound healing and transwell experiments showed that isocuB inhibited the invasion and migration of U251 cells by suppressing the activity of MMP-2/9, N-cadherin, and Vimentin. The tunnel and flow cytometry (FCM) assays revealed that isocuB induced cell apoptosis through inhibition of BCL-2. Subsequently, we conducted RT-qPCR and Western blot (WB) experiments, which revealed that PI3K/AKT and MAPK pathways might be involved in the mechanism of the inhibition isocuB on glioma. Additionally, isocuB promoted the sensitivity of glioma U251 to TMZ by inhibiting hsa-mir-1286a. Furthermore, we constructed TMZ-resistant U251 strains and demonstrated effective inhibition by isocuB against these resistant strains. Finally, we confirmed that isocuB can inhibit tumor growth in vivo through experiments on tumors in nude mice. Conclusion: IsocuB may protect against glioma by acting on the PI3K/AKT and MAPK pathways and promote the sensitivity of glioma U251 to TMZ by inhibiting hsa-mir-1286a.

Exploring genes within the glutathione peroxidase family as potential predictors of prognosis in papillary renal cell carcinoma

Abstract Objectives This research aims to explore the relationship between glutathione peroxidase (GPX) expression variations in papillary renal cell carcinoma (pRCC) and patient survival, while also developing and evaluating a customized survival prediction model based on GPX. Methods The transcriptomic dataset, including clinical parameters and GPX expression levels, is sourced from The Cancer Genome Atlas (TCGA) database, comprising 290 individuals diagnosed with pRCC. We utilized a univariate Cox regression model to select differentially expressed genes. Subsequently, we calculated risk scores through the least absolute shrinkage and selection operator (LASSO) regression. Based on the median risk score, patients were categorized into high and low-risk groups, establishing a prognostic risk model. Following this, the relationship between the risk model and the survival of pRCC patients was revealed through Kaplan–Meier survival curve analysis. The sensitivity and specificity of the predictive model were evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, chemotherapy drug sensitivity analysis was conducted on patients in the high and low-risk groups. Results A risk-scoring model was built by selecting GPX7 and GPX8. Compared to the low-risk group, individuals in the high-risk category showed significantly reduced overall survival rates (p=0.018). Additionally, validation results demonstrated the model’s good predictive accuracy. Conclusions The risk-scoring model constructed based on GPX family genes provides an innovative biomarker for forecasting the prognosis of pRCC and serves as a reference for individualized therapy in pRCC.

GJB3: a comprehensive biomarker in pan-cancer prognosis and immunotherapy prediction.

A wide range of connexins are situated between normal-normal cells, cancer-cancer cells, and cancer-normal cells. Abnormalities in connexin expression are typically accompanied by cancer development; however, no systematic studies have examined the role of Gap Junction Protein Beta 3 (GJB3) in the context of tumor progression and immunity, especially when considering a broad range of cancer types. In this study, data on GJB3 expression were gathered from Genotype-Tissue Expression, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas databases. Then, we analyzed the relationship between GJB3 expression and tumor characteristics. In vitro experiments using colony formation, EdU, CCK8, transwell migration assays, immunohistochemistry and western blot were performed to investigate the function of GJB3 in tumor progression of various cell lines. A drug sensitivity analysis of GJB3 was performed using the Genomics of Drug Sensitivity in Cancer database. Our findings demonstrate that GJB3 is widely expressed in various cancers and correlates significantly with disease stages, patient survival, immunotherapy response, and pharmaceutical guidance. Additionally, GJB3 plays a role in different cancer pathways, as well as in different immune and molecular subtypes of cancer. Co-expression of GJB3 with immune checkpoint genes was observed. Further experiments showed that knockdown of GJB3 inhibited the PI3K/AKT pathway and resulted in reduced proliferation, migration, and viability of different cancer cells. Overall, GJB3 shows potential as a molecular biomarker and therapeutic target for various cancers, particularly lung adenocarcinomas, mesothelioma, pancreatic adenocarcinoma. Thus, GJB3 may represent a new therapeutic target for a wide range of cancers.

Predicting bladder cancer survival with high accuracy: insights from MAPK pathway-related genes

The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment. To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR). Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients. The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of BLCA.