Translational cancer research最新文献

Background: N6-methyladenosine (m⁶A)-mediated epitranscriptomic pathway has been shown to contribute to chemoresistance and radioresistance. Our previous work confirmed the defense of lycorine against tamoxifen resistance of breast cancer (BC) through targeting HOXD antisense growth-associated long non-coding RNA (HAGLR). Whereas, the precise regulation among them remains to be elucidated. The aim of this study was to investigate the role of IGF2BP2-mediated m⁶A methylation in the regulation of HAGLR and its impact on lycorine's effect on tamoxifen resistance in BC.

Methods: m⁶A status was detected via methylated RNA immunoprecipitation-quantitative polymerase chain reaction (MeRIP-qPCR). Relative expression of HAGLR and IGF2BP2 were tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot analysis, respectively. Cell viability, proliferation and apoptosis were estimated via Cell Counting Kit-8 (CCK-8), colony formation and flow cytometer analysis. Interplay among IGF2BP2 and HAGLR was tested by RNA immunoprecipitation (RIP) assay. IC₅₀ value of BC cells to tamoxifen was determined by 2,5-diphenyl-2H-tetrazolium bromide (MTT) assay.

Results: Total m⁶A level in tamoxifen-resistant BC cells (TAMR/MCF-7 and TAMR/T47D) was elevated relative to corresponding parental cells and normal mammary epithelial cell line, MCF10A, either with the presence of m⁶A modifications within HAGLR sequence. Moreover, IGF2BP2-mediated m⁶A methylation drove the upregulation and stability of HAGLR in TAMR BC cells. IGF2BP2 served as a key downstream target mediating the anti-tumors of lycorine on TAMR BC. Knockdown of IGF2BP2 or HAGLR could reduce the IC₅₀ value of TAMR/MCF-7 and TAMR/T47D cells to tamoxifen.

Conclusions: Our results demonstrated that lycorine inhibits tamoxifen-resistant BC by repressing IGF2BP2-mediated m⁶A methylation of HAGLR.

Background: Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest cancers globally. Despite gemcitabine being a primary chemotherapeutic agent, many patients with PDAC develop resistance, significantly limiting treatment efficacy. This study aims to screen and validate key genes associated with gemcitabine resistance in advanced PDAC using bioinformatics analysis and clinical sample validation, thereby providing potential noninvasive biomarkers and therapeutic targets for overcoming chemoresistance.

Methods: This study used bioinformatics approaches to analyze gene expression data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, identifying differentially expressed genes (DEGs) associated with gemcitabine resistance in advanced PDAC. A total of 122 patients with advanced PDAC were selected for the study and divided into gemcitabine-sensitive and gemcitabine-resistant groups post-treatment. The expression levels of key genes in patients' serum were measured using enzyme-linked immunosorbent assay, and both univariate and multivariate analyses were performed to assess their potential as noninvasive biomarkers for predicting resistance.

Results: Ten upregulated DEGs related to gemcitabine resistance were identified. Among these genes, cathepsin E (CTSE) was significantly negatively correlated with overall survival, disease-specific survival, and progression-free interval in patients with PDAC and was thus identified as a significant key gene. Further clinical sample validation confirmed that CTSE expression level was significantly higher in the resistant group of patients with advanced PDAC compared to the sensitive group, establishing CTSE as an independent predictor of gemcitabine resistance.

Conclusions: CTSE is a key gene associated with gemcitabine resistance in advanced PDAC and shows promise as a target for enhancing responsiveness to gemcitabine treatment.

Background: Gastrointestinal stromal tumor (GIST) was very rare in the gastrointestinal (GI) tract. Most GISTs were asymptomatic at early stage. Therefore, it was of great significance to explore the prognostic factors of patients with GIST. This investigation aimed to assess the unfavorable prognostic factors for overall survival (OS) and disease-free survival (DFS) in 106 Chinese patients with GISTs.

Methods: A total of 106 Chinese patients, including 68 women and 38 men, with confirmed gastric GIST treated at the General Hospital of Ningxia Medical University in China from 2012 to 2018 were included. Kaplan-Meier analysis and Cox regression models were applied to evaluate the unfavorable prognostic risk factors for survival.

Results: Kaplan-Meier analysis demonstrated that blood type A was significantly related to poor OS (P=0.01), and tumor invasion, higher Ki-67 index, synchronous gastric cancer (GC), and tumor necrosis were significantly associated with poor DFS (all P<0.05). Multivariate analysis further demonstrated that blood type A was a significant independent prognostic factor with both OS and DFS (both P<0.05). Synchronous GC and age ≥60 years were also significant independent prognostic factor for DFS (both P<0.05).

Conclusions: Blood type A, age ≥60 years, and synchronous GC were unfavorable prognostic factors for survival in Chinese patients with gastric GISTs. The mechanism underlying the prognostic role of these factors warrants further investigation.

Background: V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim of the study was to develop a machine-learning based method to identify patients who are at high-risk of BRAFi resistance and potential biomarker.

Methods: From Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases, we collected RNA sequencing and half maximal inhibitory concentration (IC₅₀) data from 235 pan-cancer cell lines and then identified 37 significant differential expression genes associated with BRAFi resistance. Employing machine learning (ML) models, we successfully classified cell lines into resistant and sensitive groups, achieving robust performance in external validation datasets.

Results: AOX1 may play a vital part in BRAFi metabolism and resistance. Further, we found that higher mRNA expression of OXTR, H2AC13, and TBX2, and lower mRNA of SLC2A4, as detected by PCR in WM983B and SKMEL-5 cell lines, were independent risk factors for BRAFi resistance and were associated with poor prognosis.

Conclusions: We established a gene-expression model using ML methods, consisting of 37 variables based on RNA-seq database, which was externally validated and could be used to predict BRAFi resistance. Meanwhile, our findings provide valuable insights into the molecular mechanisms of BRAFi resistance, enabling the identification of high-risk patients.

Background: The pathological sub-classification of lung cancer is crucial in diagnosis, treatment and prognosis for patients. Quick and timely identification of pathological subtypes from imaging examinations rather than histological tests could help guiding therapeutic strategies. The aim of the study is to construct a non-invasive radiomics-based model for predicting the subtypes of lung cancer on brain metastases (BMs) from multiple magnetic resonance imaging (MRI) sequences.

Methods: One hundred and sixty-one patients of primary lung cancer with synchronous BMs [121 with adenocarcinoma (AD); 40 with small cell lung carcinoma (SCLC)] were enrolled in the study (129 and 32 in the training set and validation set). A total of 960 radiomics features were extracted from multiple MRI sequences [fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI), contrast-enhanced T1 weighted imaging (CE-T1WI) and contrast-enhanced susceptibility weighted imaging (CE-SWI)] and four clinical features were recorded. Forty-one key features were selected by the least absolute shrinkage selection operator (LASSO). The machine learning (ML) models for predicting AD and SCLC with radiomics features alone and with radiomics features plus clinical features were constructed using classifiers of logistic regression (LR), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost). The prediction performance of models was evaluated by accuracy (ACC), sensitivity (SEN), specificity (SPE), F1 score and area under the curves (AUC).

Results: The AUCs of LR, RF, SVM and XGBoost models were 0.8177 vs. 0.7604, 0.8177 vs. 0.7839, 0.4792 vs. 0.8594 and 0.9062 vs. 0.8750, respectively, when using radiomics features alone and radiomics features plus clinical features. In the best-performing model using XGBoost, combination of conventional MRI sequences and CE-SWI had better sub-classification performance than conventional MRI sequences.

Conclusions: Radiomics of BMs from multiple MRI sequences provides high discriminatory performance in predicting AD and SCLC using classifiers of LR, RF and XGBoost, and can serve as a potential useful tool to non-invasively distinguish pathological subtypes of lung cancer.

Background: Regulatory T cells (Tregs) play a pivotal role in the development, prognosis, and treatment of breast cancer. This study aimed to develop a Treg-associated gene signature that contributes to predict prognosis and therapy benefits in breast cancer.

Methods: Treg-associated genes were screened based on single-cell RNA-sequencing (RNA-seq) in TISCH2 database and the bulk RNA-seq in The Cancer Genome Atlas (TCGA) database. Treg-associated gene signature was identified via survival analysis, univariate cox, least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses. Immune status was assessed using single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms. Drug sensitivity was estimated using pRRophetic. Gene set enrichment analysis (GSEA) was conducted to explore the changed pathways.

Results: A total of 169 genes were identified as Treg-associated genes, and close interactions existed among these genes. Kaplan-Meier (KM) survival and univariate cox revealed 29 prognostic genes (all P<0.05), and finally a six-gene prognostic signature including TBC1D4, PMAIP1, IFNG, LEF1, MZB1 and EZR was identified by LASSO and multivariable Cox. Based on this signature, patients in high-risk group exhibited a worse survival probability than those in low-risk group in the TCGA training dataset (P<0.001). Additionally, this signature showed a moderate predictive power for 1-, 3- and 5-year survival for breast cancer patients in both training dataset [area under the curve (AUC) =0.705, 0.678 and 0.668, respectively]. Similar predictive power for 1-, 3- and 5-year survival was also observed in validation datasets. Risk scores significantly differed between subgroups divided by clinicopathologic features, especially by molecular subtypes. Patients in high- and low-risk groups showed significant differences on infiltration abundance of multiple types of immune cells (such as, activated B cells/CD8+ T cells/CD4+ T cells), immune and stromal scores (all P<0.05). Moreover, sensitivity to 83 chemotherapeutic drugs such as lapatinib, methotrexate, and gefitinib were significantly differed between the two risk groups (all P<0.001).

Conclusions: This is the first to develop a Treg-associated gene signature for breast cancer, which could predict prognosis of patients and help to identify patients who might be benefit from immunotherapy and/or chemotherapy.

Background: Ovarian cancer is a major health problem for women all over the world and tends to progress to advanced stages. Therefore, it is important to predict the early survival of patients with advanced ovarian cancer. The purpose of this study is to assist clinicians in predicting the short-term prognosis of patients with stage IV ovarian cancer in order to make optimal medical decisions.

Methods: A retrospective analysis was conducted on data from the Surveillance, Epidemiology, and End Results database, involving 3,077 patients with stage IV ovarian cancer. Univariate and multivariate logistic regression analyses were performed to identify risk factors. Using R software, relevant predictive models were constructed. The calibration, discrimination, and clinical utility of these models were assessed in a validation cohort.

Results: A nomogram model was developed utilizing four independent risk factors to predict the probability of early death in patients with stage IV ovarian cancer. The model exhibited satisfactory discrimination in both the training cohort (area under the receiver operating characteristic curve =0.816) and the validation cohort (area under the receiver operating characteristic curve =0.827). The calibration curve demonstrated a high level of predictive accuracy for the model. Furthermore, the decision curve analysis indicated that the nomogram holds clinical utility and offers a net benefit to patients within certain limitations. The predictive effectiveness of the nomogram was verified by the Kaplan-Meier survival curve.

Conclusions: We have successfully developed a nomogram and risk classification system to accurately predict the probability of early death in patients with stage IV ovarian cancer.

Background: Kinectin 1 (KTN1) is a membrane protein involved in intracellular organelle motility. However, the role of KTN1 in human pan-cancer lacks systematic analysis and evaluation. The aim of this study is to evaluate the expression profile and clinical value in human cancers by performing a pan-cancer analysis of KTN1.

Methods: The expression of KTN1 and its correlation with cancer-associated fibroblasts (CAFs) infiltration were analyzed in TIMER2.0. The survival analysis, pathological stage correlation, and co-expression gene correlation analysis of KTN1 were performed on GEPIA2.0. We investigated the protein expression level of KTN1 in tumors through UALCAN. The cBioportal platform was used to analyze the variation frequency and type of KTN1. We used STRING database for protein-protein interaction (PPI) network analysis. Cell Counting Kit-8 (CCK8) and colony formation and transwell assay were performed to investigate the proliferation and migration abilities of head and neck squamous cell carcinoma (HNSC) cells with KTN1 knockdown.

Results: KTN1 was differentially expressed in 12 kinds of cancer tissues compared with correspondent normal tissues. Meanwhile, the high expression of KTN1 was negatively correlated with the prognosis of HNSC, adrenocortical carcinoma (ACC), and liver hepatocellular carcinoma (LIHC). Further analysis suggested that patients with KTN1 mutations had better overall survival (OS) and progression-free survival than those without mutations among several cancers. Moreover, the level of CAFs and KTN1 expression were significantly correlated in 12 types of cancer. Mechanically, co-expression analysis showed the positive association between KTN1 and KTN1 antisense RNA 1 (KTN1-AS1), MNAT1 component of CDK activating kinase (MNAT1), N-alpha-acetyltransferase 30 (NAA30), protein phosphatase 2 regulatory subunit B'epsilon (PPP2R5E), and proteasome 26S subunit (PSMC6), which are mainly involved in the protein kinase AMP-activated catalytic subunit alpha 1 (AMPK) signaling pathway that regulates the progression of tumors.

Conclusions: The functional experiment revealed that KTN1 promotes the proliferation and metastasis of HNSC cells. The pan-cancer analysis of KTN1 revealed its significance in different cancers, which provides a new marker for the diagnosis and prognosis of cancers.

Breast cancer is one of the most common cancers among women. Nowadays postoperative adjuvant chemotherapy is the mainstay for clinical treatment of breast cancer. However, the emergence of multidrug resistance (MDR) in breast cancer has become a main reason for the failure of clinical chemotherapy. Multiple studies have demonstrated that the formation of MDR in breast cancer is combined with ATP-binding transporters, which are the proteins that can lead to the drug resistance by pumping out chemotherapeutic drugs to reduce their intracellular accumulation. This kind of protein mainly includes P-glycoprotein (Pgp, ABCB1, MDR1), multidrug resistance-associated protein (MRP-1, ABCC1) and breast cancer resistance protein (BCRP, ABCG2). The former two transporters have been investigated deeply and widely, while the molecular mechanism of BCRP regulation of breast cancer drug resistance has relatively not much been explored in the area of breast cancer. How to design a novel, effective and non-toxic BCRP inhibitor to reverse the MDR of breast cancer, and boost the success rate of chemotherapy is a serious challenge at present. A detailed overview of the molecular role of BCRP-mediated breast cancer MDR and its inhibitors reported in recent years is provided in this article. The expectation is to provide ideas for clinically addressing MDR in breast cancer, and further guide the direction for the development of new anti-breast cancer drugs and reversal of breast cancer MDR drugs.

Background: Prostate adenocarcinoma (PRAD) is a common male urinary system cancer, and its targeted treatment is difficult. This study aimed to investigate the value of B cell senescence-related genes in PRAD prognosis.

Methods: PRAD sample expression and clinical information were downloaded from The Cancer Genome Atlas (TCGA) Program and Gene Expression Omnibus (GEO) databases, and B cell senescence-related gene sets were obtained from the Genecards library. The prognostic model was constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses of PRAD differentially expressed genes significantly related to B cell senescence. The Kaplan-Meier (K-M) survival curve and receiver operating characteristic (ROC) curve were drawn to verify the survival rate difference between the high and low risk score groups of the model. The differences of immune characteristics between high and low risk groups were evaluated by single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT. The tumor mutation burden (TMB) score was used to assess the variation in genomic mutations across the groups. Small molecule drugs were screened through the GDSC library. Ultimately, in order to examine the risk assessment model's practicality, a nomogram was created.

Results: Three genes WNT16, INS and BMP2 related to PRAD progression and B cell senescence were selected to construct a prognostic risk assessment model. The K-M survival curve and ROC curve verified the good performance in evaluating the prognosis of patients. In terms of immune characteristics, the high-risk score group of the model showed a higher overall immune score and immune cell infiltration level, and the high-risk group showed a relatively higher TP53 and TTN mutation frequency. Drug sensitivity analysis showed that the high-risk group had higher resistance to Camptothecin, Cisplatin and WIKI4 drugs. At last, the nomogram that is created using pathological characteristics in conjunction with the risk score can reliably assess the prognosis of patients with PRAD.

Conclusions: This study constructed and verified a B cell senescence-related gene model that can predict prognosis of PRAD. More importantly, it provides a reference standard for guiding the prognosis of PRAD patients.