Translational cancer research最新文献

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.

Background: Esophageal squamous cell carcinoma (ESCC) is a malignancy for which the incidence and mortality rates are among the highest worldwide. This study aimed to look for potential biomarkers that affect the prognosis of patients with ESCC.

Methods: The target gene IFIT3 was screened through differential expression gene analysis, cluster analysis, enrichment analysis, and construction of a protein-protein interaction (PPI) network, and then validated through clinical patient tissue RNA extraction and reverse transcription quantitative polymerase chain reaction (qRT-PCR). The Mann-Whitney U test and Kaplan-Meier analysis were used to investigate the correlation between the relative expression of IFIT3 and the clinical pathological information and prognosis of ESCC patients.

Results: Gene Expression Omnibus (GEO) detected 279 differentially expressed genes (DEGs) in ESCC and paracancerous tissues. Cluster analysis and enrichment analysis showed that cluster 4 played an important role in immune-related functions. PPI network analysis showed that IFIT3 was the hub gene in cluster 4. Clinical patient tissue samples confirmed the differential expression of IFIT3 in ESCC and paracancerous tissues. Mann-Whitney U test showed that the relative expression of IFIT3 was significantly correlated with clinicopathological information in patients with ESCC. Kaplan-Meier survival analysis showed that the disease-free survival (DFS) time and overall survival (OS) time of patients with low expression of IFIT3 were significantly longer than those of patients with high expression of IFIT3, and the correlations were more significant in some subgroups. The Cox proportional hazards model showed that lymph node metastasis was an independent risk factor for the prognosis of ESCC patients.

Conclusions: IFIT3 is differentially expressed in the cancerous and paracancerous tissues of ESCC, and the relative expression level of IFIT3 is correlated with the clinical pathological characteristics and prognosis of ESCC. IFIT3 can be used as a potential biomarker for patient risk stratification and local regional metastasis in ESCC.

Background and objective: Basal cell carcinoma (BCC) is the most common malignancy of humankind, characterized by its low propensity for metastasis and its high recurrence rate. Surgical intervention is the predominant therapeutic approach. However, for cases of locally advanced BCC (laBCC) and metastatic BCC (mBCC), systematic therapy may be the first option. In recent years, tumor immunotherapy has garnered significant attention within the scientific community. And it has progressively demonstrated its efficacy in the treatment of laBCC and mBCC. This review aims to summarize the characteristics of immune microenvironment, biomarkers, and immunotherapies of BCC, and provide a reference for further research on BCC immunotherapy.

Methods: We searched literature in PubMed database and Web of Science and considered all study types written in English from 2013 to 2024.

Key content and findings: The alteration of the immune microenvironment is a pivotal factor in the progression of BCC. The expression levels of sex determining region Y (SRY)-box 2 (SOX2) and matrix metalloproteinases (MMPs) have emerged as potential prognostic biomarkers for BCC. And they are promising therapeutic targets for laBCC and mBCC. For patients presenting with laBCC or mBCC, a spectrum of immunotherapeutic approaches is being explored, including inhibition of the programmed death receptor 1 (PD-1) and programmed death ligand 1 (PD-L1), blockade of cytotoxic T-lymphocyte antigen 4 (CTLA-4), lymphocyte activation gene 3 (LAG-3) inhibition therapy, the use of chimeric antigen receptor (CAR)-T cells, and vaccination. Cemiplimab is the first immune checkpoint inhibitor (ICI) approved by the Food and Drug Administration for refractory BCC, marking a major breakthrough in BCC immunotherapy.

Conclusions: Immunotherapies have shown efficacy in clinical studies. In the future, more multicenter studies with large samples are needed to further explore the efficacy and safety of immunotherapy for BCC.

Background: Lung cancer is the most prevailing oncological disease worldwide. Visceral pleural invasion (VPI) has been proven to be a poor prognosis factor for early-stage non-small cell lung carcinoma (NSCLC) patients. However, there remains some debate regarding whether NSCLC patients with tumor size (TS) ranging from >2.0 to 3.0 cm and VPI should be considered for postoperative treatment. This study compared the prognosis of T2a and T2b NSCLC patients, specifically focusing on those with VPI and TS ranging from >2.0-3.0 cm to emphasize the severity of the disease. Additionally, the impact of adjuvant therapies on the outcome of these patients was discussed.

Methods: This retrospective research utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, which provided a comprehensive dataset of 10,452 patients diagnosed with pN0M0 NSCLC with TS intervals of >2.0-5.0 cm between 2010 and 2019. The SEER database, renowned for its expansive and population-based cancer data, provides a robust platform for researchers to access a large cohort of patients diagnosed with NSCLC. Survival probabilities were calculated by the Kaplan-Meier method and compared between groups with Log-rank test. Univariate and multivariate logistic analyses were used to identify independent risk factors of VPI.

Results: Patients with NSCLC and TS between >2.0 and 3.0 cm, along with VPI, had a worse 5-year overall survival rate compared to those at T2a stage (49.1% vs. 56.8%, P=0.03) and T2b stage (45.4% vs. 64.2%, P<0.0001). However, no statistical significance was observed when comparing patients with TS range between >2.0 and 3.0 cm and presenting with VPI to those staged T2b and received adjuvant chemotherapy (48.4% vs. 48.5%, P=0.54). Patients with clinical stage of T1c and VPI positive had significantly better prognosis after receiving chemotherapy (34.5% vs. 55.2%, P<0.001). Logistic analysis indicated that age older than 65 years old, poor differentiated and undifferentiated, as well as sub-lobectomy resection were independent risk factors for VPI in NSCLC.

Conclusions: Postoperative chemotherapy can improve the prognosis of patients with TS ranging from >2.0 to 3.0 cm with VPI. According to the analysis of OS based on the postoperative chemotherapy, patients with NSCLC featuring TS extend from >2.0 to 3.0 cm and VPI may be classified within stage IIA. Consequently, the consideration of postoperative chemotherapy for this patient cohort may be warranted.

Radiation- (radio-)recurrent prostate cancer poses a significant challenge in clinical management due to its complexity and varied treatment responses. The recurrence of prostate cancer following radiotherapy necessitates a nuanced management strategy that considers disease stage and aggressiveness, patient health status, and prior treatment modalities. Androgen deprivation therapy (ADT), a cornerstone in the management of regional or distant relapse, often initiates the therapeutic cascade, effectively suppressing tumor growth by targeting androgen signaling. Second-line antiandrogen therapies such as abiraterone and enzalutamide, in conjunction with ADT, exhibit considerable clinical efficacy by delaying disease progression and ameliorating symptoms. However, in the absence of regional or distant disease, local relapse after radiation may be best managed with local salvage therapy. Salvage radical prostatectomy (SRP) may be considered in select cases of local recurrence, providing a potentially curative option. Salvage radiation therapy (RT), such as stereotactic body RT (SBRT), low-dose-rate (LDR), or high-dose-rate (HDR) brachytherapy (BT) is another viable option for localized recurrences. Other local treatments, such as cryotherapy, high-intensity focused ultrasound (HIFU) and irreversible electroporation (IRE) have been applied as salvage local therapy for radio-recurrent prostate cancer with promising results. Notwithstanding, exploring new avenues for improved outcomes and personalized treatment strategies as well as clinical trials investigating novel therapeutic agents and combination therapies remain imperative for these men. This comprehensive review aims to examine the current landscape of therapeutic approaches and emerging strategies for managing radio-recurrent prostate cancer.

Background: Breast cancer (BRCA) constitutes one of the principal causes of death among women. The objective of this study was to explore the impact of glucose-aminotransferase 3 (GCNT3) on the growth, invasion, and metastasis of BRCA cells. Additionally, the aim of this research was to clarify the underlying molecular mechanisms through which GCNT3 influences the development and progression of BRCA and to ascertain the potential of GCNT3 as a novel BRCA biomarker.

Methods: Analysis involved data sourced from the The Cancer Genome Atlas database (TCGA). Expression levels of GCNT3 were measured using Western blot analysis and immunohistochemistry (IHC). Additionally, cell functionality tests were performed posttransfection with GCNT3-specific interference plasmids to assess the influence of GCNT3 in BRCA by using EdU assay, transwell assay, and flow cytometric assay, as well as PI3K/AKT signaling pathway.

Results: GCNT3 levels were notably elevated in BRCA tissues compared to adjacent noncancerous tissues. Reducing GCNT3 expression significantly diminished the proliferation, invasion, and migration capabilities of BRCA cells (P<0.05) and concurrently increased apoptosis (P<0.05). The data also indicated that GCNT3 may be involved in activating the PI3K/AKT signaling pathway.

Conclusions: Elevated GCNT3 expression in BRCA tissues suggests the potential of GCNT3 to be a biomarker for predicting BRCA prognosis. The regulation of p-PI3K and p-AKT levels by GCNT3 appears to considerably inhibit BRCA cell development and progression.

Minorities participate less than White people in a variety of research settings limiting the generalizability of the research results. The driving forces behind the lower participation rates are multifactorial and vary by race. Further compounding these driving forces are past inequities and violations of trust by the healthcare system. Addressing these issues is crucial to equitably accruing within clinical trials and subsequently addressing the lack of generalizable results being produced. Despite legislation being enacted to increase the enrollment of minorities in clinical trials, the participation rates remain low with cancer clinical trials being particularly disparate in terms of equitable representation. As prostate cancer disproportionately affects Black men, it is imperative that prostate cancer clinical trials enroll an equitable number of Black men. Previous trials including the Prostate, Lung, Colorectal, and Ovarian multicenter randomized trial and the Prostate Cancer Prevention Trial both involved concerted efforts to address the relatively low participation rate of minority men, but both were not successful in that regard. To facilitate equal access and ensure the appropriate participation of minorities in prostate cancer clinical trials, various interventions from additional safety assurances to consenting patients as a family unit have been employed, depending on the community. Overall, progress is being made in equitably accruing clinical trials, but there remains more work to be done.

Background: Liver metastasis (LM) is of vital importance in making treatment-related decisions in patients with colorectal cancer (CRC). The aim of our study was to develop and validate prediction models for LM in CRC by making use of machine learning.

Methods: We selected patients diagnosed with CRC from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Four machine-learning methods, eXtreme gradient boost (XGB), decision tree (DT), random forest (RF), and support vector machine (SVM), were employed to develop a predictive model. The receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves and calibration curves were adopted to evaluate the model performance. The SHapley Additive exPlanation (SHAP) technique was chosen for visual analysis to enhance the interpretation of the outcomes of models.

Results: A total of 51,632 patients suffering from CRC were selected from the SEER database. Excellent accuracy of machine learning models was showed from ROC curves. In both the training and validation cohorts, calibration curves for the likelihood of LM demonstrated a high degree of concordance between model prediction and actual observation. The DCA indicated that each machine learning model can yield net benefits for both treat-none and treat-all strategies. Carcinoembryonic antigen (CEA) and N stage were identified as the most significant risk factors for LM based on the SHAP summary plot of the RF and XGB models.

Conclusions: The XGB and RF were the best machine learning models among the four algorithms, of which CEA and N stage were identified as the most important risk factors related to LM.