Chemotherapeutic Drug Sensitivity Research Articles

Ex Vivo Intestinal Organoid Models: Current State-of-the-Art and Challenges in Disease Modelling and Therapeutic Testing for Colorectal Cancer.

Despite improvements in participation in population-based screening programme, colorectal cancer remains a major cause of cancer-related mortality worldwide. Targeted interventions are desirable to reduce the health and economic burden of this disease. Two-dimensional monolayers of colorectal cancer cell lines represent the traditional in vitro models for disease and are often used for diverse purposes, including the delineation of molecular pathways associated with disease aetiology or the gauging of drug efficacy. The lack of complexity in such models, chiefly the limited epithelial cell diversity and differentiation, attenuated mucus production, lack of microbial interactions and mechanical stresses, has driven interest in the development of more holistic and physiologically relevant in vitro model systems. In particular, established ex vivo patient-derived explant and patient-derived tumour xenograft models have been supplemented by progress in organoid and microfluidic organ-on-a-chip cultures. Here, we discuss the applicability of advanced culturing technologies, such as organoid systems, as models for colorectal cancer and for testing chemotherapeutic drug sensitivity and efficacy. We highlight current challenges associated with organoid technologies and discuss their future for more accurate disease modelling and personalized medicine.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma.

Disulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored. In this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis. This signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells. In general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

Non-cytotoxic levels of resveratrol enhance the anticancer effects of cisplatin by increasing the methyltransferase activity of CARM1 in human cancer cells

BackgroundResveratrol (RSVL) is a plant-derived polyhydroxyphenolic compound with excellent anticancer properties, alone or in combination with other chemotherapeutic drugs. However, the anticancer mechanism of RSVL is diverse and high concentrations are often required for RSVL to exert its anticancer effect, which would also adversely affect normal cells. PurposeThe main objective of this study is to investigate the molecular mechanism of how non-cytotoxic concentrations of RSVL enhance the anticancer effect of cisplatin involving a newly identified RSVL-binding protein. MethodsCell viability of cell lines from three cancer types exposed to RSVL and/or cisplatin was measured by NBB staining assay. RSVL-binding proteins were identified using RSVL-bound CNBr-activated Sepharose 4B beads coupled with LC-MS/MS, and the binding between RSVL and novel RSVL-binding protein was further confirmed with an in vitro pull-down assay. The expression of proteins was examined by immunoblot analysis, and the activity of methyltransferase was evaluated by in vitro methylation assay. The methylation level of H3R17 in the gene promoter was investigated using ChIP-qPCR. Bioinformatics analysis was conducted to identify pathway enrichment of genes, predict drug sensitivity, and analyze the survival of cancer patients. ResultsLow doses of RSVL might promote cancer cell growth whereas high doses of RSVL showed cytotoxic effects on normal cells. When co-treated with a lower cisplatin dose, non-cytotoxic RSVL levels showed synergistic anticancer effects. Here, coactivator-associated arginine methyltransferase 1 (CARM1) was identified as a novel RSVL-binding protein, and we showed that the upregulation of CARM1 increased the sensitivity of cancer cells to RSVL. Interestingly, we found that CARM1 was essential in the RSVL-induced sensitivity of cisplatin. Further molecular mechanistic studies revealed that RSVL could stabilize CARM1 protein, resulting in the upregulation and increased methyltransferase activity of CARM1. Additionally, we showed that the methylation levels of H3R17 in the promoter of p21, a downstream gene of CARM1 involving cell cycle arrest, were significantly increased after RSVL treatment. Finally, data from our bioinformatics analysis suggested that CARM1 could be utilized as a potential biomarker for chemotherapeutic drug sensitivity and prognosis in cancers. ConclusionsThis study identified CARM1 as a RSVL-binding protein for the first time and elucidated the potential roles of CARM1 in enhancing the efficacy of cisplatin by low doses of RSVL, which could have important clinical implications.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment.

Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer. To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules. This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines. The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) (b P < 0.01), moderately associated with tumor stage (T) (a P < 0.05), and significantly correlated with residual tumor (R) status (b P < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer. The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Integrated analyses reveal IDO1 as a prognostic biomarker coexpressed with PD-1 on tumor-associated macrophages in esophageal squamous cell carcinoma.

Inhibition of indolamine-2,3-dioxygenase 1 (IDO1) has been proposed as a promising strategy for cancer immunotherapy; however, it has failed in clinical trials. Macrophages in the tumor microenvironment (TME) contribute to immune escape and serve as potential therapeutic targets. This study investigated the expression pattern of IDO1 in TME and its impact on prognosis and therapeutic response of patients with esophageal squamous cell carcinoma (ESCC). RNA sequencing data from 95 patients with ESCC from The Cancer Genome Atlas (TCGA) database were used to explore the prognostic value of IDO1. Bioinformatics tools were used to estimate scores for stromal and immune cells in tumour tissues, abundance of eight immune cell types in TME, and sensitivity of chemotherapeutic drugs and immune checkpoint (IC) blockage. The results were validated using digitalized immunohistochemistry and multiplexed immunofluorescence in ESCC tissue samples obtained from our clinical center. TCGA and validation data suggested that high expression of IDO1 was associated with poor patient survival, and IDO1 was an independent prognostic factor. IDO1 expression positively correlated with macrophages in TME and PDCD1 within diverse IC genes. Single-cell RNA sequencing data analysis and multiplexed immunofluorescence verified the coexpression of IDO1 and PD-1 in tumor-associated macrophages (TAMs). Patients with high IDO1 expression showed increased sensitivity to various chemotherapeutic drugs, while were more likely to resist IC blockage. This study identifies IDO1 as an independent prognostic indicator of OS in patients with ESCC, reveals a compelling connection of IDO1, PD-1, and TAMs, and explores the sensitivity of patients with high IDO1 expression to chemotherapeutic drugs and their resistance to IC blockade. These findings open new avenues for potential targets in ESCC immunotherapy.

Comprehensive analysis of cuproptosis-related genes involved in prognosis and tumor microenvironment infiltration of colorectal cancer.

Colorectal cancer (CRC) is a common malignancy, with high incidence and high mortality rates. Cuproptosis, a novel form of copper-induced programmed cell death, contributes to tumor progression. However, whether cuproptosis-related genes (CRGs) play a role in CRC remains unclear. This study aims to elucidate the role of CRGs in CRC development, patient prognosis, and immune response. We performed bioinformatics analysis of the differential expression of CRGs between CRC and normal tissues. Least absolute shrinkage and selection operator (LASSO), and univariate and multivariate Cox analyses were employed to identify risk factors, which were used to construct a risk score model. Patients with CRC were categorized into high- and low-risk groups based on their median risk scores. Receiver operating characteristic curve analysis was used to verify the predictive accuracy of the risk model. A nomogram was developed for CRC through univariate and multivariate Cox regression analyses. The chemotherapeutic drug sensitivity was compared between patients with high and low CDKN2A/DLAT expression using the Wilcoxon rank-sum test. Spearman's correlation and TISIDB database analyses were conducted to determine relationships between CDKN2A or DLAT and immune cell infiltration. Eight of ten identified CRGs exhibited significant differential expression between CRC and normal tissues. Among the eight significant differential expression CRGs, CDKN2A and DLAT were identified as independent risk factors for predicting overall survival (OS) in CRC. Patients with CRC in the low-risk group had longer OS than those in the high-risk group. The risk score model had good predictive accuracy for OS. Based on CDKN2A, DLAT and some clinical characteristics, a prognostic nomogram was developed to predict OS for CRC patients and showed good predictive ability. CDKN2A and DLAT expressions were significantly associated with chemotherapeutic drug sensitivity and immune cell infiltration in CRC, and the molecular subtypes and immune subtypes differed between CDKN2A and DLAT. Our research revealed the prognostic value of CRGs, particularly CDKN2A and DLAT, in CRC and demonstrated the relationship between CDKN2A/DLAT and immune infiltration in CRC, thereby contributing to the outcome evaluation of patients with CRC and identifying novel targets for CRC immunotherapy.

Developing Patient-Derived 3D-Bioprinting models of pancreatic cancer

IntroductionPancreatic cancer (PC) remains a challenging malignancy, and adjuvant chemotherapy is critical in improving patient survival post-surgery. However, the intrinsic heterogeneity of PC necessitates personalized treatment strategies, highlighting the need for reliable preclinical models. ObjectivesThis study aimed to develop novel patient-derived preclinical PC models using three-dimensional bioprinting (3DP) technology. MethodsPatient-derived PC models were established using 3DP technology. Genomic and histological analyses were performed to characterize these models and compare them with corresponding patient tissues. Chemotherapeutic drug sensitivity tests were conducted on the PC 3DP models, and correlations with clinical outcomes were analyzed. ResultsThe study successfully established PC 3DP models with a modeling success rate of 86.96%. These models preserved genomic and histological features consistent with patient tissues. Drug sensitivity testing revealed significant heterogeneity among PC 3DP models, mirroring clinical variability, and potential correlations with clinical outcomes. ConclusionThe PC 3DP models demonstrated their utility as reliable preclinical tools, retaining key genomic and histological characteristics. Importantly, drug sensitivity profiles in these models showed potential correlations with clinical outcomes, indicating their promise in customizing treatment strategies and predicting patient prognoses. Further validation with larger patient cohorts is warranted to confirm their potential clinical utility.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

MiR-149-3p Enhances Drug Sensitivity of AML Cells by Inhibiting Warburg Effect Through PI3K/AKT Pathway.

Acute myeloid leukemia (AML) is a kind of heterogeneous hematologic malignancy with high incidence, which is usually treated by intensive and maintenance treatment with large dose of conventional chemotherapy drugs. However, cell resistance is still an unsolved problem. The abnormal expression of miRNAs is closely related to the pathogenesis and progression of AML, and affects the drug resistance of cancer cells. miR-149-3p plays an important role in the resistance of cancer cells to cisplatin, and plays an excellent anti-tumor activity. By studying the function of miR-149-3p, it is expected to find new therapeutic methods to reverse chemotherapy resistance. In order to explore the mechanism of action of miR-149-3p on AML chemotherapeutic drug sensitivity, we explored the relationship between the Warburg effect and AML chemotherapeutic drug resistance. Based on AML cells, transfection of miR-149-3p inhibitor/NC and Warburg effect inhibitor (2DG) and PI3K/AKT pathway inhibitor (LY294002) were used to investigate the mechanism of IFN-γ regulating chemotherapy resistance of AML cells through Warburg effect. Down-regulation of miR-149-3p significantly inhibited drug sensitivity of AML cells. Down-regulation of miR-149-3p significantly promoted proliferation and invasion of AML cells while inhibiting apoptosis by up-regulating the expression of Bcl-2 and down-regulating the expression of Bax. Down-regulation of miR-149-3p significantly promoted the expression of Warburg effect-related proteins hexokinase 2 (HK2), lactate dehydrogenase A (LDHA), and Glucose transporter 1 (GLUT1), glucose consumption, lactic acid, and intracellular ATP production. After inhibiting the Warburg effect with 2DG, the effect of miR-149-3p was inhibited, suggesting that upregulation of miR-149-3p reversed AML cell resistance by inhibiting the Warburg effect. In addition, miR-149-3p interacted with AKT1. Down-regulation of miR-149-3p increased the expression of inosine phosphate 3 kinase (PI3K), protein kinase B (AKT), and multi-drug resistance protein (MDR1). LY294002 inhibited the expression of these proteins, and down-regulation of miR-149-3p reversed the effect of LY294002 and improved the drug resistance of cells. Upregulation of miR-149-3p expression may potentially be a therapeutic target for AML resistance. It has been shown to inhibit PI3K/AKT pathway activation, thereby inhibiting the Warburg effect, and affecting cell proliferation, apoptosis, and drug resistance.

Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.

Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index (MPCDI) based on 19 PCD patterns using two machine learning algorithms for risk stratification, prognostic prediction, construction of nomograms, immune cell infiltration analysis, and chemotherapeutic drug sensitivity analysis. As a result, in the TCGA-COAD, GSE17536, and GSE29621 cohorts, the MPCDI can effectively distinguished survival outcomes in CRC patients and served as an independent factor for CRC patients. We then explored the immune infiltration landscape in two groups using the nine algorithms and found more overall immune infiltration in the high-MPCDI group. TIDE scores suggested that the increased immune evasion potential and immune checkpoint inhibition therapy may be less effective in the high-MPCDI group. Immunophenoscores indicated that anti-PD1, anti-cytotoxic T-lymphocyte associated antigen 4 (anti-CTLA4), and anti-PD1-CTLA4 combination therapies are less effective in the high-MPCDI group. In addition, the high-MPCDI group was more sensitive to AZD1332, Foretinib, and IGF1R_3801, and insensitive to AZD3759, AZD5438, AZD6482, Erlotinib, GSK591, IAP_5620, and Picolinici-acid, which suggests that the MPCDI can guide drug selection for CRC patients. As a new clinical classifier, the MPCDI can more accurately distinguish CRC patients who benefit from immunotherapy and develop personalized treatment strategies for CRC patients.

Caspase-4 in glioma indicates deterioration and unfavorable prognosis by affecting tumor cell proliferation and immune cell recruitment

Gliomas are the most common malignant tumors of the central nervous system, accounting for approximately 80% of all malignant brain tumors. Accumulating evidence suggest that pyroptosis plays an essential role in the progression of cancer. Unfortunately, the effect of the pyroptosis-related factor caspase-4 (CASP4) on immunotherapy and drug therapy for tumors has not been comprehensively investigated. In this study, we systematically screened six hub genes by pooling differential pyroptosis-related genes in The Cancer Genome Atlas (TCGA) glioma data and the degree of centrality of index-related genes in the protein–protein interaction network. We performed functional and pathway enrichment analyses of the six hub genes to explore their biological functions and potential molecular mechanisms. We then investigated the importance of CASP4 using Kaplan–Meier survival analysis of glioma patients. TCGA and the Chinese Glioma Genome Atlas (CGGA) databases showed that reduced CASP4 expression leads to the potent clinical deterioration of glioma patients. Computational analysis of the effect of CASP4 on the infiltration level and recruitment of glioma immune cells revealed that CASP4 expression was closely associated with a series of tumor-suppressive immune checkpoint molecules, chemokines, and chemokine receptors. We also found that aberrant CASP4 expression correlated with chemotherapeutic drug sensitivity. Finally, analysis at the cellular and tissue levels indicated an increase in CASP4 expression in glioma, and that CASP4 inhibition significantly inhibited the proliferation of glioma cells. Thus, CASP4 is implicated as a new prognostic biomarker for gliomas with the potential to further guide immunotherapy and chemotherapy strategies for glioma patients.

Integrating single-cell RNA-seq to identify fibroblast-based molecular subtypes for predicting prognosis and therapeutic response in bladder cancer.

Bladder cancer (BLCA) is a highly aggressive and heterogeneous disease, posing challenges for diagnosis and treatment. Cancer immunotherapy has recently emerged as a promising option for patients with advanced and drug-resistant cancers. Fibroblasts, a significant component of the tumor microenvironment, play a crucial role in tumor progression, but their precise function in BLCA remains uncertain. Single-cell RNA sequencing (scRNA-seq) data for BLCA were obtained from the Gene Expression Omnibus database. The R package "Seurat" was used for processing scRNA-seq data, with uniform manifold approximation and projection (UMAP) for downscaling and cluster identification. The FindAllMarkers function identified marker genes for each cluster. Differentially expressed genes influencing overall survival (OS) of BLCA patients were identified using the limma package. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between high- and low-risk groups were investigated. RT-qPCR and immunohistochemistry validated the expression of prognostic genes. Fibroblast marker genes identified three molecular subtypes in the testing set. A prognostic signature comprising ten genes stratified BLCA patients into high- and low-score groups. This signature was validated in one internal and two external validation sets. High-score patients exhibited increased immune cell infiltration, elevated chemokine expression, and enhanced immune checkpoint expression but had poorer OS and a reduced response to immunotherapy. Six sensitive anti-tumor drugs were identified for the high-score group. RT-qPCR and immunohistochemistry showed that CERCAM, TM4SF1, FN1, ANXA1, and LOX were highly expressed, while EMP1, HEYL, FBN1, and SLC2A3 were downregulated in BLCA. A novel fibroblast marker gene-based signature was established, providing robust predictions of survival and immunotherapeutic response in BLCA patients.

Prognostic value of oxidative phosphorylation-related genes in hepatocellular carcinoma

PurposeHepatocellular carcinoma (HCC) is the most prevalent malignancies worldwide. Recently, oxidative phosphorylation (OXPHOS) has received extensive concern as an emerging target in antitumor therapy. However, the OXPHOS-involved underlying genes and clinical utilization in HCC remain worth exploring. The present research aimed to create an OXPHOS-relevant signature in HCC.Patients and methodsIn this study, the prognostic signature genes linked with OXPHOS were identified, and prognostic models were built using least absolute shrinkage and selection operator (LASSO) cox regression analysis. Furthermore, the combination study of immune microenvironment and signature genes looked into the involvement of immune cells in signature-based genes in HCC. Following that, chemotherapeutic drug sensitivity and immunotherapy analysis was implemented to predict clinical efficacy in HCC patients. Finally, clinical samples were collected to measure the expression of OXPHOS-related signature genes.ResultsFollowing a series of screens, six prognostic signature genes related with OXPHOS were identified: MRPS23, MPV17, MAPK3, IGF2BP2, CDK5, and IDH2, on which a risk model was built. The findings revealed a significant drop in the survival rate of HCC patients as their risk score increased. Meanwhile, independent prognostic study demonstrated that the risk score could accurately identify HCC patients. Immuno-microenvironmental correlation research suggested that the prognostic characteristics could serve as a reference index for both immunotherapy and chemotherapy. Finally, RT-qPCR exhibited a trend in signature gene expression that was consistent with the results.ConclusionIn this study, a total of six prognostic genes associated with OXPHOS were selected and a prognostic model was constructed, providing an essential reference for the study of OXPHOS in HCC.

Crosstalk between circBMI1 and miR-338-5p/ID4 inhibits acute myeloid leukemia progression.

BMI1 polycomb ring finger proto-oncogene (BMI1) is involved in the pathogenesis of different cancers, including acute myeloid leukemia (AML). However, the role of the circular RNA of BMI1 (circBMI1) has not been studied. Our study aimed to investigate the role and mechanism of circBMI1 in AML. circBMI1 was significantly decreased in bone marrow mononuclear cells aspirated from patients with AML. Receiver operating characteristic curve analysis showed that circBMI1 could distinguish patients with AML from controls. By overexpressing and knocking down circBMI1 in HL-60 cells, we found that circBMI1 inhibited cell proliferation, promoted apoptosis, and increased chemotherapeutic drug sensitivity in AML. Experiments using severe combined immune-deficient mice and circBMI1 transgenic mice showed that mice with circBMI1 overexpression had lower white blood cell counts, which suggested less severe AML invasion. RNA immunoprecipitation and dual-luciferase reporter assay revealed binding sites among circBMI1, miR-338-5p, and inhibitor of DNA-binding protein 4 (ID4). Rescue experiments proved that circBMI1 inhibited AML progression by binding to miR-338-5p, which affected the expression of ID4. By coculturing exosomes extracted from circBMI1-HL-60 and small interfering circBMI1-HL-60 cells with HL-60 cells, we found that exosomes from circBMI1-HL-60 cells showed tumor-suppressive effects, namely inhibiting HL-60 proliferation, promoting apoptosis, and increasing chemotherapeutic drug sensitivity. Exosomes from small interfering circBMI1-HL-60 cells showed the opposite effects. circBMI1 may act as an exosome-dependent tumor inhibitor. circBMI1, a potential biomarker for clinical diagnosis, acts as a tumor suppressor in AML by regulating miR-338-5p/ID4 and might affect the pathogenesis of AML by exosome secretion.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy

BackgroundOsteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored.MethodDifferential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models.ResultsWe established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1.ConclusionsWe developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

A novel signature of seven aging-related genes for risk stratification, prognosis prediction and benefit evaluation of chemotherapy, and immunotherapy in elderly patients with lung adenocarcinoma

BackgroundAging, a multifaceted biological process, is thought to be associated with lung adenocarcinoma (LUAD) development and progression. However, it is unclear whether aging-related genes (ARGs) can predict tumor risk, chemotherapy and immunotherapy benefits, and prognosis in LUAD patients at different ages. MethodsGene expression datasets and clinical information of LUAD patients were downloaded from TCGA and GEO database. Univariate and multivariate Cox regression, and lasso algorithm were employed to identify the ARG signatures. Patients were stratified into high-risk and low-risk groups to evaluate the predictive accuracy using Kaplan-Meier curves, ROC curves, and time-dependent AUC. A nomogram was established to predict the survival probability. GSEA revealed potential pathways, and CIBERSORT indicated different immunologic status. TIDE score was used to predict the potential tumor response to immune checkpoint inhibitors, and GDSC was employed to evaluate the sensitivity of chemotherapeutic drugs. The correlation of TIDE score and patient age, as well as that of ARGs and patient age was investigated. And cell Culture and RT-qPCR for external validation for key gene. ResultsA novel gene signature based on seven ARGs was established, including BMP15, CD79A, CDKN3, CDX2, COL1A1, DKK1, and GRIK2. Our model demonstrated exceptional prediction accuracy for elderly LUAD patients of 71–90 years old. A nomogram model was constructed to predict the survival probability, and the C-index value was 0.737, indicating our prognostic nomogram model has high accuracy. Through external RT-qPCR validation, we found that CD79A expression in H1299 was higher than that of BEAS-2B. And novel immunotherapy and chemotherapy regimens were accordingly proposed for the elderly LUAD patients. ConclusionWe identified a novel gene signature based on seven ARGs for risk stratification, prognosis prediction and benefit evaluation of immunotherapy and chemotherapy in elderly LUAD patients.

An anoikis-related gene signature predicts prognosis, drug sensitivity, and immune microenvironment in cholangiocarcinoma

BackgroundCholangiocarcinoma is a malignant invasive biliary tract carcinoma with a poor prognosis. Anoikis-related genes are prognostic features of a variety of cancers. However, the value of prognostication and therapeutic effect of anoikis-related genes in cholangiocarcinoma have not been reported. The aim of this research was developing an ARGs signature associated with cholangiocarcinoma patients. MethodsWe introduced transcriptome data to discover genes that were differentially expressed in cholangiocarcinoma. Subsequently, WGCNA was utilized to screen critical module genes in reference to anoikis. The univariate Cox, Lasso regression and Kaplan-Meier survival were executed to build a prognostic signature. We further performed gene functional enrichment, immune microenvironment and immunotherapy analysis between two risk subgroups. Finally, the pRRophetic algorithm was applied to compare the half inhibitory concentration value of several drugs. ResultsA grand total of 1844 genes with differential expression related to the cholangiocarcinoma patients were identified. Furthermore, we obtained 2678 key module genes related to anoikis. Then, a prognostic signature was developed using the 6 prognostic genes (FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1). Independent prognostic analysis showed that risk score and alcohol could function as separate prognostic variables. We found cetain distinction in the immune microenvironment between the two risk subgroups. Moreover, immunotherapy evaluation showed that the anoikis-related gene signature could be applied as a therapy predictor. Finally, Chemotherapeutic drug sensitivity results showed that the low-risk group responded better to bosutinib, gefitinib, gemcitabine, and paclitaxel, while the high-risk group responded better to axitinib, cisplatin, and imatinib. ConclusionThe prognostic signature comprised of FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1 based on anoikis-related genes was established, which provided theoretical basis and reference value for the research and treatment of cholangiocarcinoma.

Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells

The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.

Identification and validation of a platelet-related signature for predicting survival and drug sensitivity in multiple myeloma.

Background: Significant progress has been achieved in the management of multiple myeloma (MM) by implementing high-dose therapy and stem cell transplantation. Moreover, the prognosis of patients has been enhanced due to the introduction of novel immunomodulatory drugs and the emergence of new targeted therapies. However, predicting the survival rates of patients with multiple myeloma is still tricky. According to recent researches, platelets have a significant impact in affecting the biological activity of tumors and are essential parts of the tumor microenvironment. Nonetheless, it is still unclear how platelet-related genes (PRGs) connect to the prognosis of multiple myeloma. Methods: We analyzed the expression of platelet-related genes and their prognostic value in multiple myeloma patients in this study. We also created a nomogram combining clinical metrics. Furthermore, we investigated disparities in the biological characteristics, immunological microenvironment, and reaction to immunotherapy, along with analyzing the drug susceptibility within diverse risk groups. Results: By using the platelet-related risk model, we were able to predict patients' prognosis more accurately. Subjects in the high-risk cohort exhibited inferior survival outcomes, both in the training and validation datasets, as compared to those in the low-risk cohort (p < 0.05). Moreover, there were differences in the immunological microenvironments, biological processes, clinical features, and chemotherapeutic drug sensitivity between the groups at high and low risk. Using multivariable Cox regression analyses, platelet-related risk score was shown to be an independent prognostic influence in MM (p < 0.001, hazard ratio (HR) = 2.001%, 95% confidence interval (CI): 1.467-2.730). Furthermore, the capacity to predict survival was further improved when a combined nomogram was utilized. In training cohort, this outperformed the predictive value of International staging system (ISS) alone from a 5-years area under curve (AUC) = 0.668 (95% CI: 0.611-0.725) to an AUC = 0.721 (95% CI: 0.665-0.778). Conclusion: Our study revealed the potential benefits of PRGs in terms of survival prognosis of MM patients. Furthermore, we verified its potential as a drug target for MM patients. These findings open up novel possibilities for prognostic evaluation and treatment choices for MM.

Integrated analysis of single-cell and bulk RNA sequencing to construct a CD8 + T cell-related immune gene signature in pancreatic cancer

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor that responds poorly to immunotherapy. The pivotal influence of CD8 + T-cell infiltration on immunotherapy has been documented in various solid tumors. However, the specific contribution of CD8 + T cell-associated immune genes (TIGs) to the tumor immune microenvironment remains unclear. Methods: We obtained CD8 + T cell-related immune genes from the INNATE and IMMPORT databases. Univariate analysis and lasso regression analysis were utilized to screen hub TIGs and develop a prognostic signature, the TIGs score. This score was used to evaluate prognosis, immunocyte infiltration, cancer-associated signaling pathways, and the potential responsiveness of immunotherapy. The transcriptomic data and single-cell data from the GSE183795 and GE212966 datasets are employed to validate the reliability of the findings. Results: Our findings suggest that patients with low TIGs scores have stronger immune effector functions and immune checkpoint activation, resulting in a more favorable response to PD-L1 inhibitors. TIGs scores were significantly correlated with various molecular characteristics and clinical outcomes, such as tumor mutation burden, multiple tumor-associated pathways, and chemotherapeutic drug sensitivity. PSME2 was identified as a potential prognostic biomarker for predicting survival in patients with PDAC. Conclusions: This study elucidates the intricate regulatory mechanisms of TIGs within the tumor immune microenvironment of pancreatic cancer. Our findings strongly suggest that the TIGs score is a robust prognostic marker for prognosis and immunotherapy responsiveness. Additionally, targeting PSME2 may offer a novel avenue for enhancing the effectiveness of immunotherapy in pancreatic cancer.