Translational cancer research最新文献

Background: Tyrosine kinase inhibitors (TKIs) have become the preferred drugs for the treatment of chronic phase (CP) chronic myeloid leukemia (CML). This study aims to compare the safety and efficacy of different TKIs as first-line treatments for CML using network meta-analysis (NMA), providing a basis for the precise clinical use of TKIs.

Methods: A systematic search was conducted on PubMed, Cochrane Library, Embase, China National knowledge Infrastructure (CNKI), Wanfang, Chinese Science and Technology Periodical Databases (VIP), SinoMed and ClinicalTrials.gov to include RCTs that compared the different TKIs as first line treatment for CML. The search timeline was from inception to 21 July 2023. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the frequentist NMA methods, the efficacy and safety of different TKIs were compared, including the rates of major molecular response (MMR), complete cytogenetic response (CCyR), all grade adverse events, grade 3 or higher hematologic adverse events and liver toxicity.

Results: A total of 25 RCTs involving 6,823 patients with CML and 6 types of TKIs were included. In terms of efficacy, second-generation TKIs such as dasatinib, nilotinib, and radotinib showed certain advantages in improving patients' MMR and CCyR compared to imatinib. Additionally, imatinib 800 mg provided better MMRs and CCyRs than imatinib 400 mg. As far as safety was concerned, there was no significant difference in the incidence of all grade adverse events among the different TKIs. All TKIs can cause serious grade 3-4 hematologic adverse events, including anemia, thrombocytopenia, and neutropenia. Dasatinib more likely caused anemia, bosutinib thrombocytopenia, and imatinib neutropenia, whereas nilotinib and flumatinib might have better safety profiles in terms of severe hematologic adverse events. For liver toxicity, radotinib 400 mg and imatinib 800 mg, respectively, had the highest likelihood of ranking first in incidence rates of all grade ALT and AST elevation.

Conclusions: In CML, second-generation TKIs are more clinically effective than imatinib even if this last drug has a relatively better safety profile. Thus, as each second-generation TKI has a distinct clinical efficacy and safety, and is associated with different economic factors, its choice should be dictated by the specific patient clinical conditions (patient's specific disease characteristics, comorbid conditions, potential drug interactions, as well as their adherence). Nevertheless, due to the limited number of original research, additional high-quality studies are needed to achieve any firm conclusion on which second-generation TKI is the best choice for that peculiar patient.

Background: The incidence of diffuse large B-cell lymphoma (DLBCL) in children is increasing globally. Due to the immature immune system in children, the prognosis of DLBCL is quite different from that of adults. We aim to use the multicenter large retrospective analysis for prognosis study of the disease.

Methods: For our retrospective analysis, we retrieved data from the Surveillance, Epidemiology and End Results (SEER) database that included 836 DLBCL patients under 18 years old who were treated at 22 central institutions between 2000 and 2019. The patients were randomly divided into a modeling group and a validation group based on the ratio of 7:3. Cox stepwise regression, generalized Cox regression and eXtreme Gradient Boosting (XGBoost) were used to screen all variables. The selected prognostic variables were used to construct a nomogram through Cox stepwise regression. The importance of variables was ranked using XGBoost. The predictive performance of the model was assessed by using C-index, area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. The consistency of the model was evaluated by using a calibration curve. The clinical practicality of the model was verified through decision curve analysis (DCA).

Results: ROC curve demonstrated that all models except the non-proportional hazards and non-log linearity (NPHNLL) model, achieved AUC values above 0.7, indicating high accuracy. The calibration curve and DCA further confirmed strong predictive performance and clinical practicability.

Conclusions: In this study, we successfully constructed a machine learning model by combining XGBoost with Cox and generalized Cox regression models. This integrated approach accurately predicts the prognosis of children with DLBCL from multiple dimensions. These findings provide a scientific basis for accurate clinical prognosis prediction.

Background: Colon adenocarcinoma (COAD) is among the most prevalent malignancies. Changes to N6-methyladenosine (m6A), the most common RNA modification, can affect how COAD develops. Furthermore, the involvement of long noncoding RNA (lncRNA) in COAD is significant, and it exhibits a close association with m6A modification. Nevertheless, the prognostic significance of lncRNAs that are related to m6A modification in COAD remains unclear. This study aims to establish a m6A-related lncRNA pair signature and reveal its prognostic value in COAD.

Methods: The current study utilized data from The Cancer Genome Atlas (TCGA) to investigate the predictive significance of m6A-related lncRNA pair signatures in COAD. The identification of m6A-related lncRNAs was conducted through co-expression analysis using the Pearson correlation coefficient. Then, the lncRNA pairs related to prognosis were identified using univariate Cox regression analysis. Receiver operating characteristic (ROC) curves were produced using the least absolute shrinkage and selection operator (LASSO) penalized with Cox analysis to predict overall survival (OS) in order to build a risk score prognostic model. The relationship among the risk scoring model and clinical characteristics, immune-related variables, and medication sensitivity was examined after identifying independent prognostic factors.

Results: Thirty-five of the 319 lncRNA pairings associated with m6A were linked to a pattern that predicted risk ratings. It was verified that the risk score model was a reliable predictor that stood alone from clinicopathological features. Differences between high- and low-risk groups were found in clinicopathological traits, immune-related variables, and medication sensitivity analysis according to correlation analyses.

Conclusions: Based on paired differentially expressed m6A-related lncRNAs, the proposed COAD prognostic model demonstrated potential clinical predictive value.

Background: Mitochondria are the center of cellular metabolism. The relationship between mitochondria and diseases has also been studied for a long time. However, the prognostic role of mitochondrial-related genes (MRGs) in patients with glioma and their biological effects are still unclear. The aim of the study was to construct a mitochondria-related model to assess prognosis and potential biological effects like immune infiltration, gene pathway and mutation, and give some predictive chemotherapeutic agents.

Methods: The data of 675 patients from The Cancer Genome Atlas (TCGA) database were used to identify MRG signature and construct a prognostic model. After validating its robustness in Chinese Glioma Genome Atlas (CGGA), two risk groups derived from the prognostic model were then conducted with Gene Set Enrichment Analysis (GSEA), immune status, mutation status and chemotherapeutic agents prediction.

Results: The prognostic model built from six gene signatures can successfully predict the prognosis and reflect clinicopathological characteristics. Patients in high-risk group displayed significantly worse overall survival (OS), immunosuppression effects, and mutation markers with worse prognosis. Twelve chemotherapeutic agents with strongly correlated sensitivity and risk scores were selected as potential agents.

Conclusions: The novel MRG signatures (TYMP, TSFM, MGME1, BOLA3, TRMT5, NDUFA9) can predict prognosis and immunological status in glioma.

Background: Previously, long non-coding RNA (lncRNA) gene AP001469.3 was reported to participate in the construction of an immune-related lncRNA signature, which showed promising clinical predictive value in colorectal cancer (CRC) patients. However, the clinical and immunological significance and biological function of AP001469.3 in CRC remain unclear. In this study, we aim to explore the roles of AP001469.3 in CRC progression, thereby opening an avenue for CRC treatment.

Methods: Our study collected data from The Cancer Genome Atlas (TCGA) database and investigated the role of AP001469.3 in CRC through bioinformatics analysis. Cell-type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) methods evaluated the immune infiltration. The biological functions of AP001469.3 in CRC were validated by in vitro experiments. Gene set enrichment analysis (GSEA) was used to estimate the enrichment of functional pathways and gene signatures.

Results: In this work, high expression of AP001469.3 was found in CRC and was positively associated with tumor-node-metastasis (TNM) stage in CRC. AP001469.3 expression had a strong relationship with microsatellite instability (MSI) in colon adenocarcinoma (COAD). Additionally, AP001469.3 expression was associated with StromalScore, ImmuneScore, ESTIMATEScore, immune cell infiltration (ICI) levels and immune checkpoint (ICP) genes expression in CRC. Subsequent results showed that immunotherapy could be more effective in CRC patients with low-AP001469.3 expression using the immunophenoscore (IPS). We confirmed that the transcript of AP001469.3 gene ENST00000430259 was highly expressed in CRC tissues and cell lines. In vitro experiments indicated that ENST00000430259 knockdown reduced the proliferation, migration and invasion of CRC cells. Finally, our GSEA results showed that the majority of the differentially enriched signaling pathways between the high- and low-AP001469.3 expression groups were immune-related.

Conclusions: Taken together, our study demonstrates that lncRNA gene AP001469.3 is associated with immunological characteristics in CRC and promotes malignant progression of CRC. Moreover, AP001469.3 can be potentially used as an immunotherapeutic indicator and a therapeutic target for CRC patients.

Background: Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted.

Methods: RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219.

Results: Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression

Background: Acute myeloid leukemia (AML) is the second most frequently occurring type of leukemia in adults. Despite breakthroughs in genetics, the prognosis of AML patients remains dismal. The aim of this study is to find new therapeutic targets and diagnostic markers for AML and to explore their mechanisms of action.

Methods: The expression patterns of integrin subunit alpha M (ITGAM) were investigated across different cell types using the Human Protein Atlas (HPA) database. The ITGAM levels across cancer types were analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Prognostic correlations in AML individuals were evaluated using The Cancer Genome Atlas (TGCA) database. ITGAM-associated functions were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The AML cells were transfected with short-hairpin RNA targeting ITGAM or a control, and subsequently subjected to analysis in order to ascertain the impact of ITGAM on proliferation and apoptosis.

Results: The expression of ITGAM was significantly higher in the AML patient samples compared to the control samples. High ITGAM expression was significantly associated with poor overall survival (OS). The knockdown of ITGAM in the AML cells resulted in a decrease in proliferation and an increase in apoptosis. This was accompanied by cell cycle arrest at the G1 phase and a downregulation of protein production for cyclin D1, cyclin E1, cyclin-dependent kinase 2 (CDK2), and cyclin-dependent kinase 4 (CDK4). A pathway analysis and a western blot analysis revealed that ITGAM positively regulated mitogen-activated protein kinase (MAPK) signaling by silencing attenuated p38 MAPK (P38), c-Jun N-terminal kinase (JNK), and extracellular signal-regulated kinase (ERK) phosphorylation, while the total protein levels remained unchanged.

Conclusions: ITGAM can serve as a potential prognostic biomarker and therapeutic target for AML. ITGAM production was elevated in AML and indicated poor survival. Silencing ITGAM suppressed AML cell viability and induced apoptosis by blocking cell cycle progression, likely by impeding the activation of the MAPK pathway. Further investigations that directly target the ITGAM-MAPK axis may offer novel strategies for mitigating AML pathogenesis and overcoming chemotherapy resistance.

Background: Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years.

Methods: Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.

Results: Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.

Conclusions: We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.

Background: Colorectal cancer (CRC) remains the leading cause of cancer death worldwide. Less than half of the patients are diagnosed when the cancer is locally advanced. Several studies have shown that intelectin-1 (ITLN1) can serve as a key prognostic and therapeutic target for CRC. The purpose of this study was to investigate the clinical value of ITLN1 in CRC and to analyse its potential as a predictive biomarker for CRC.

Methods: Colon adenocarcinoma (COAD) is the main type of CRC. COAD project in The Cancer Genome Atlas (TCGA) database served as the training cohort, and GSE39582 series in the Gene Expression Omnibus (GEO) database served as the external independent validation cohort. First, the difference in the expression level of ITLN1 between COAD tissue and normal tissue was analysed, and the results were verified via immunohistochemistry. The relationship between ITLN1 expression and the prognosis of COAD patients was evaluated via the heatmap and the Kaplan-Meier (KM) curve. The ITLN1 coexpressed gene set obtained by Pearson correlation analysis was used. The prognostic signatures that were significantly correlated with survival status were screened by Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Finally, a nomogram related to ITLN1 was constructed based on the risk score of the prognostic signature and routine clinicopathological variables.

Results: ITLN1 is significantly underexpressed in tumour tissues and can be used as a valuable tool to distinguish COAD. The high-expression group of ITLN1 was verified to have a greater survival rate. ITLN1 is significantly associated with a good prognosis in COAD patients. Six candidate genes (ITLN1 and MORC2, SH2D7, LGALS4, ATOH1, and NAT2) were selected for use in the Cox-LASSO regression analysis to calculate the risk score. Finally, a nomogram was constructed with a comprehensive risk score and clinicopathologic factors to successfully predict and verify the 1-year, 3-year, and 5-year survival probability.

Conclusions: Our study established ITLN1 as an effective tool for CRC screening, diagnosis, and prognostic assessment, provided a basis for further study of the molecular function of ITLN1, and provided new insights for the mechanistic exploration and treatment of CRC.

Background: RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed.

Methods: RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database.

Results: We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy.

Conclusions: Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score mo