Role of rs873601 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy.

BACKGROUND: Lung cancer is still the most lethal malignancy in the world, according to the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined with immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate is always affected by the adverse reactions and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms that can affect the response to chemotherapy and clinical outcomes in lung cancer patients. OBJECTIVE: The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis of platinum-based chemotherapy in lung cancer patients. PATIENTS AND METHODS: We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients via Sequenom MassARRAY. We used Cox proportional hazard models, state, and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. RESULTS: We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treated with platinum-based chemotherapy (p = 0.031). There were some polymorphisms that were related to the prognosis in specific subgroups of lung cancer. Rs873601 showed a great influence on the prognosis of patients more than 55 years, Small Cell Lung Cancer (SCLC), and smoking patients. Rs2444933 was associated with prognosis in age less than 55 years, SCLC, metastasis, and stage III/IV/ED patients. Rs3740051 played an important role in the prognosis of SCLC and metastasis patients. Rs1869641 was involved in the prognosis of SCLC patients. Rs1051685 was related to the prognosis in non-metastasis patients. CONCLUSION: The ERCC5 rs873601 (G>A) was a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy.

Roles of TRPM7 in ovarian cancer.

Ovarian cancer stands as the prevailing gynecologic malignancy, afflicting over 313,959 individuals annually worldwide, accompanied by more than 207,252 fatalities. Perturbations in calcium signaling contribute significantly to the pathogenesis of numerous cancers, including ovarian cancer, wherein alterations in calcium transporter expression have been reported. Overexpression of TRPM7, a prominent calcium transporter, has been linked to adverse prognostic outcomes in various cancer types. The focus of this comprehensive review centers around delineating the oncogenic role of TRPM7 in cancer development and exploring its therapeutic potential as a target in combating this disease. Notably, TRPM7 fosters cancer invasion, metastasis, and uncontrolled cell proliferation, thereby perpetuating the expansion and reinforcement of these malignant entities. Furthermore, this review takes ovarian cancer as an example and summarizes the "dual-mode" regulatory role of TRPM7 in cancer. Within the domain of ovarian cancer, TRPM7 assumes the role of a harsh tyrant, firmly controlling the calcium ion signaling pathway and metabolic reprogramming pathways.

eIF3a sustains non-small cell lung cancer stem cell-like properties by promoting YY1-mediated transcriptional activation of ß-catenin.

Cancer stem cells (CSCs) are the leading cause of recurrence and poor prognosis in non-small cell lung cancer (NSCLC). Eukaryotic translation initiation factor 3a (eIF3a) participates in many tumor development processes, such as metastasis, therapy resistance, and glycolysis, all of which are closely associated with the presence of CSCs. However, whether eIF3a maintains NSCLC-CSC-like properties remains to be elucidated. In this study, eIF3a was highly expressed in lung cancer tissues and was linked to poor prognosis. eIF3a was also highly expressed in CSC-enriched spheres compared with adherent monolayer cells. Moreover, eIF3a is required for NSCLC stem cell-like traits maintenance in vitro and in vivo. Mechanistically, eIF3a activates the Wnt/ß-catenin signaling pathway, promoting the transcription of cancer stem cell markers. Specifically, eIF3a promotes the transcriptional activation of ß-catenin and mediates its nuclear accumulation to form a complex with T cell factor 4 (TCF4). However, eIF3a has no significant effect on protein stability and translation. Proteomics analysis revealed that the candidate transcription factor, Yin Yang 1 (YY1), mediates the activated effect of eIF3a on ß-catenin. Overall, the findings of this study implied that eIF3a contributes to the maintenance of NSCLC stem cell-like characteristics through the Wnt/ß-catenin pathway. eIF3a is a potential target for the treatment and prognosis of NSCLC.

CircRNAs in tumor immunity and immunotherapy: Perspectives from innate and adaptive immunity.

Tumor immunotherapy is a new therapeutic approach that has been evolving in the last decade and has dramatically changed the treatment options for cancer. Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) with high stability, tissue-specific and cell-specific expression. There is growing evidence that circRNAs are involved in the regulation of both adaptive and innate immunity. They play important roles in tumor immunotherapy by affecting macrophage, NK and T cell function. The high stability and tissue specificity make them ideal candidate biomarkers for therapeutic effects. CircRNAs also represent one of promising targets or adjuvant for immunotherapy. Investigations in this field progress rapidly and provide essential support for the diagnosis, prognosis and treatment guidance of cancers in the future. In this review, we summarize the role of circRNAs on tumor immunity from the viewpoint of innate and adaptive immunity, and explore the role of circRNAs in tumor immunotherapy.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Association of variations in the CAT and prognosis in lung cancer patients with platinum-based chemotherapy.

PURPOSE: To explore the relationship between ATM, ATR and CAT polymorphisms and prognosis of lung cancer patients received platinum-based chemotherapy. METHODS: 404 patients with lung cancer who received platinum-chemotherapy were enrolled and DNA typing was performed. Cox regression analysis and stratification analyses was performed to assess relationships between OS and PFS with SNPs genotypes. The prognosis of lung adenocarcinomaand squamous cell carcinomapatients was analyzed with The Cancer Genome Atlas (TCGA) database according to the grouping of CAT expression. RESULTS: CAT rs769217 was significantly related to PFS of patients with lung cancer who received platinum-chemotherapy. In the Additive model, rs769217 was associated with PFS (HR = 0.747, 95% CI = 0.581-0.960, p = 0.023). In the Dominant model, CT and TT genotypes led to lung cancer progression 0.738 times more than CC genotype. In stratification analyses of association between CAT rs769217 polymorphisms and PFS, the HR of patients at stage IV in additive model was 0.73, and HR was 0.745 (p = 0.034) in dominant model. For OS analyses, HR was 0.672 in the older lung cancer patients (>55 years old) in additive model. Meanwhile, in the Dominant model, it was found that the older patients with CT and TT genotypes had better prognosis, and the risk of death after receiving platinum-based chemotherapy was 0.692 times that of patients with CC genotype (p = 0.037). TCGA data shows that LUAD patients with high CAT expression have longer OS (p = 0.020). CONCLUSION: CAT rs769217 is significantly related to PSF of platinum-based chemotherapy in lung cancer patients and may be a biomarker for predicting the prognosis of lung cancer patients with platinum-based chemotherapy.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Effects of Glycolysis-Related Genes on Prognosis and the Tumor Microenvironment of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a common and deadly malignancy worldwide. Current treatment methods for hepatocellular carcinoma have many disadvantages; thus, it is urgent to improve the efficacy of these therapies. Glycolysis is critical in the occurrence and development of tumors. However, survival and prognosis biomarkers related to glycolysis in HCC patients remain to be fully identified. Methods: Glycolysis-related genes (GRGs) were downloaded from "The Molecular Signatures Database" (MSigDB), and the mRNA expression profiles and clinical information of HCC patients were obtained from TCGA. Consensus clustering was performed to classify the HCC patients into two subgroups. We used the least absolute shrinkage and selection operator (LASSO) regression analysis to construct the risk signature model. Kaplan-Meier (K-M) survival analysis was performed to evaluate the prognostic significance of the risk model, and the receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy. The independent prediction ability of the risk model was validated by univariate and multivariate Cox regression analyses. The differences of immune infiltrates and relevant oncogenic signaling between different risk groups were compared. Finally, biological experiments were performed to explore the functions of screened genes. Results: HCC patients were classified into two subgroups, according to the expression of prognostic-related GRGs. Almost all GRGs categorized in cluster 2 showed upregulated expressions, whereas GRGs in cluster 1 conferred survival advantages. GSEA identified a positive correlation between cluster 2 and the glycolysis process. Ten genes were selected for risk signature construction. Patients were assigned to high-risk and low-risk groups based on the median risk score, and K-M survival analysis indicated that the high-risk group had a shorter survival time. Additionally, the risk gene signature can partially affect immune infiltrates within the HCC microenvironment, and many oncogenic pathways were enriched in the high-risk group, including glycolysis, hypoxia, and DNA repair. Finally, in vitro knockdown of ME1 suppressed proliferation, migration, and invasion of hepatocellular carcinoma cells. Conclusion: In our study, we successfully constructed and verified a novel glycolysis-related risk signature for HCC prognosis prediction, which is meaningful for classifying HCC patients and offers potential targets for the treatment of hepatocellular carcinoma.

Pan-cancer analyses identify DCBLD2 as an oncogenic, immunological, and prognostic biomarker.

Discoidin, CUB, and LCCL domain-containing protein 2 (DCBLD2) is a two-domain transmembrane protein-coding gene located on chromosome 3, the protein expressed by which acts as the membrane receptor of semaphorin and vascular endothelial growth factor during the development of axons and blood vessels. Although several research evidences at the cellular and clinical levels have associated DCBLD2 with tumorigenesis, nothing is known regarding this gene from a pan-cancer standpoint. In this study, we systematically analyzed the influence of DCBLD2 on prognosis, cancer staging, immune characteristics, and drug sensitivity in a variety of cancers based on a unified and standardized pan-cancer dataset. In addition, we performed GO enrichment analyses and KEGG analyses of DCBLD2-related genes and DCBLD2-binding proteins. Our results showed that DCBLD2 is a potential oncogenic, immunological as well as a prognostic biomarker in terms of pan-cancer, and is expected to contribute to the improvement of tumor prognosis and the development of targeted therapy.

Integrated Analysis of Expression Profile and Potential Pathogenic Mechanism of Temporal Lobe Epilepsy With Hippocampal Sclerosis.

Objective: To investigate the potential pathogenic mechanism of temporal lobe epilepsy with hippocampal sclerosis (TLE+HS) by analyzing the expression profiles of microRNA/ mRNA/ lncRNA/ DNA methylation in brain tissues. Methods: Brain tissues of six patients with TLE+HS and nine of normal temporal or parietal cortices (NTP) of patients undergoing internal decompression for traumatic brain injury (TBI) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60K. The cDNA was labeled and hybridized to the Agilent LncRNA+mRNA Human Gene Expression Microarray V3.0,4 × 180K. For methylation detection, the DNA was labeled and hybridized to the Illumina 450K Infinium Methylation BeadChip. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change >2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor. BrainSpan database was used to screen for signatures that were not differentially expressed in normal human hippocampus and cortex (data from BrainSpan), but differentially expressed in TLE+HS' hippocampus and NTP' cortex (data from our cohort). The strategy "Guilt by association" was used to predict the prospective roles of each important hub mRNA, miRNA, or lncRNA. Results: A significantly negative correlation (r < -0.5) was found between 116 pairs of microRNA/mRNA, differentially expressed in six patients with TLE+HS and nine of NTP. We examined this regulation network's intersection with target gene prediction results and built a lncRNA-microRNA-Gene regulatory network with structural, and functional significance. Meanwhile, we found that the disorder of FGFR3, hsa-miR-486-5p, and lnc-KCNH5-1 plays a key vital role in developing TLE+HS.

Genetic Variants in Double-Strand Break Repair Pathway Genes to Predict Platinum-Based Chemotherapy Prognosis in Patients With Lung Cancer.

Objective: The purpose of this study was to investigate the associations of genetic variants in double-strand break (DSB) repair pathway genes with prognosis in patients with lung cancer treated with platinum-based chemotherapy. Methods: Three hundred ninety-nine patients with lung cancer who received platinum-based chemotherapy for at least two cycles were included in this study. A total of 35 single nucleotide polymorphisms (SNPs) in DSB repair, base excision repair (BER), and nucleotide excision repair (NER) repair pathway genes were genotyped, and were used to evaluate the overall survival (OS) and the progression-free survival (PFS) of patients who received platinum-based chemotherapy using Cox proportional hazard models. Results: The PFS of patients who carried the MAD2L2 rs746218 GG genotype was shorter than that in patients with the AG or AA genotypes (recessive model: p = 0.039, OR = 5.31, 95% CI = 1.09-25.93). Patients with the TT or GT genotypes of TNFRSF1A rs4149570 had shorter OS times than those with the GG genotype (dominant model: p = 0.030, OR = 0.57, 95% CI = 0.34-0.95). We also investigated the influence of age, gender, histology, smoking, stage, and metastasis in association between SNPs and OS or PFS in patients with lung cancer. DNA repair gene SNPs were significantly associated with PFS and OS in the subgroup analyses. Conclusion: Our study showed that variants in MAD2L2 rs746218 and TNFRSF1A rs4149570 were associated with shorter PFS or OS in patients with lung cancer who received platinum-based chemotherapy. These variants may be novel biomarkers for the prediction of prognosis of patients with lung cancer who receive platinum-based chemotherapy.

Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy.

BACKGROUND: Seizures are a common symptom in glioma patients, and they can cause brain dysfunction. However, the mechanism by which glioma-related epilepsy (GRE) causes alterations in brain networks remains elusive. OBJECTIVE: To investigate the potential pathogenic mechanism of GRE by analyzing the dynamic expression profiles of microRNA/ mRNA/ lncRNA in brain tissues of glioma patients. METHODS: Brain tissues of 16 patients with GRE and 9 patients with glioma without epilepsy (GNE) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60 K. The cDNA was labeled and hybridized to the Agilent LncRNA + mRNA Human Gene Expression Microarray V3.0, 4 × 180 K. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change > 2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor. RESULTS: We found that 3 differentially expressed miRNAs (miR-10a-5p, miR-10b-5p, miR-629-3p), 6 differentially expressed lncRNAs (TTN-AS1, LINC00641, SNHG14, LINC00894, SNHG1, OIP5-AS1), and 49 differentially expressed mRNAs play a vitally critical role in developing GRE. The expression of GABARAPL1, GRAMD1B, and IQSEC3 were validated more than twofold higher in the GRE group than in the GNE group in the validation cohort. Pathways including ECM receptor interaction and long-term potentiation (LTP) may contribute to the disease's progression. Meanwhile, We built a lncRNA-microRNA-Gene regulatory network with structural and functional significance. CONCLUSION: These findings can offer a fresh perspective on GRE-induced brain network changes.

Construction of m6A-Related lncRNA Prognostic Signature Model and Immunomodulatory Effect in Glioblastoma Multiforme.

Background: Glioblastoma multiforme (GBM), the most prevalent and aggressive of primary malignant central nervous system tumors (grade IV), has a poor clinical prognosis. This study aimed to assess and predict the survival of GBM patients by establishing an m6A-related lncRNA signaling model and to validate its validity, accuracy and applicability. Methods: RNA sequencing data and clinical data of GBM patients were obtained from TCGA data. First, m6A-associated lncRNAs were screened and lncRNAs associated with overall survival in GBM patients were obtained. Subsequently, the signal model was established using LASSO regression analysis, and its accuracy and validity are further verified. Finally, GO enrichment analysis was performed, and the influence of this signature on the immune regulation response and anticancer drug sensitivity of GBM patients was discussed. Results: The signature constructed by four lncRNAs AC005229.3, SOX21-AS1, AL133523.1, and AC004847.1 is obtained. Furthermore, the signature proved to be effective and accurate in predicting and assessing the survival of GBM patients and could function independently of other clinical characteristics (Age, Gender and IDH1 mutation). Finally, Immunosuppression-related factors, including APC co-inhibition, T-cell co-inhibition, CCR and Check-point, were found to be significantly up-regulated in GBM patients in the high-risk group. Some chemotherapeutic drugs (Doxorubicin and Methotrexate) and targeted drugs (AZD8055, BI.2536, GW843682X and Vorinostat) were shown to have higher IC50 values in patients in the high-risk group. Conclusion: We constructed an m6A-associated lncRNA risk model to predict the prognosis of GBM patients and provide new ideas for the treatment of GBM. Further biological experiments can be conducted on this basis to validate the clinical value of the model.

Association of variations in the Fanconi anemia complementation group and prognosis in Non-small cell lung cancer patients with Platinum-based chemotherapy.

PURPOSE: To explore the associations between FANC (FANCA, FANCC, FANCE, FANCF, and FANCJ) single nucleotide polymorphisms (SNPs) and prognosis of non-small cell lung cancer (NSCLC) patients with platinum-based chemotherapy. METHODS: According to the inclusion criteria, we selected 395 DNA samples from NSCLC patients for genotyping and combined with clinical data for Cox regression analysis and stratification analyses to assess relationships between overall survival (OS) and progression free survival (PFS) with SNPs genotypes. RESULTS: The results revealed that patients with FANCE rs6907678 TT genotype have a longer OS than TC and CC genotype (Additive model: P = 0.004, HR = 1.696, 95% CI = 1.186-2.425). In stratification analyses, Longer PFS is found in female, age ≤ 55 years old and non-smoking patients with FANCE rs6907678 TT genotype, and patients with TT genotypes were significantly had longer OS in male, age ＞55 years old, non-smoking, squamous cell carcinoma and stage IV stratification. CONCLUSION: Our data demonstrates that patients with FANCE rs6907678 TT genotype are contributed to better prognosis. FANCE rs6907678 may be used as a clinical biomarker for predicting the prognosis of NSCLC patients with platinum-based chemotherapy.

eIF3a Regulates Colorectal Cancer Metastasis via Translational Activation of RhoA and Cdc42.

Tumor metastasis is the major cause of tumor relapse and cancer-associated mortality in colorectal cancer, leading to poor therapeutic responses and reduced survival. eIF3a was previously described as an oncogene. However, its role in colorectal cancer progression and metastasis has not yet been fully investigated. In this study, the expression specificity and predictive value of eIF3a were investigated in clinical samples. The effects of eIF3a on cell proliferation and migration were verified in vivo and in vitro, respectively. The underlying molecular mechanism was revealed by western blotting, immunofluorescence, RNA-binding protein immunoprecipitation, and dual-luciferase reporter gene assays. The results showed that eIF3a was significantly overexpressed in tumor tissues compared with adjacent normal tissues. High eIF3a expression was correlated with tumor metastasis and overall survival. Downregulation of eIF3a obviously inhibited the proliferation and motility of malignant cells in vitro and in vivo. Mechanistically, eIF3a regulates Cdc42 and RhoA expression at the translation level, which further affects pseudopodia formation and actin cytoskeleton remodeling. Taken together, eIF3a accelerates the acquisition of the migratory phenotype of cancer cells by activating Cdc42 and RhoA expression at the translational level. Our study identified eIF3a as a promising target for inhibiting colorectal cancer metastasis.

eIF3a-PPP2R5A-mediated ATM/ATR dephosphorylation is essential for irinotecan-induced DNA damage response.

OBJECTIVES: The individual differences and pervasive resistance seriously hinder the optimization of irinotecan-based therapeutic effectiveness. Eukaryotic translation initiation factor 3a (eIF3a) plays a key role in tumour occurrence, prognosis and therapeutic response. This study focused on the role of eIF3a in irinotecan-induced DNA damage response. MATERIALS AND METHODS: The cck8 cell viability and clone survival analyses were used to test the regulatory role of eIF3a on irinotecan sensitivity in HT29 and CACO2 cell lines in vitro. This regulatory role was also verified in vivo by conducting subcutaneous xenograft model. Irinotecan-induced DNA damage, cell cycle arrest and apoptosis were tested by flow cytometry analysis, TUNEL staining, western blot and comet assays. The immunofluorescence, co-IP, luciferase reporter assay, RIP and flow cytometric analyses were carried out to investigate the underline mechanism. RESULTS: We demonstrated that eIF3a continuously activates ATM/ATR signal by translationally inhibiting PPP2R5A, a phosphatase that directly dephosphorylates and inactivates ATM/ATR after DNA repair complete. Suppression of PPP2R5A resulted in chronic ATM/ATR phosphorylation and activation, impairing DNA repair and enhancing irinotecan sensitivity. CONCLUSIONS: Our study suggested eIF3a with a high potential to influence phenotypic functions, which may contribute substantially to the early identification of susceptible individuals and the provision of personalized medication to irinotecan-treated patients.