Translational cancer research最新文献

Background: Research interest into regulation of gene expression by physical activity and its effect on cancer prognosis has intensified. This study investigated the role of an exercise-related gene, NUP155, in the progression of non-small cell lung cancer (NSCLC) and its potential as therapy target.

Methods: Using the GSE41914 dataset, which includes data related to exercise, and the Cancer Genome Atlas (TCGA)-NSCLC dataset, we identified differentially expressed genes (DEGs) and selected NUP155 as a hub gene for further analysis. NUP155 expression levels were measured in NSCLC cell lines and normal lung cells using in vitro assays. The functional roles of NUP155 were investigated through small interfering RNA (siRNA) knockdown experiments, assessing effects on migration, cell proliferation, invasion, and apoptosis. The involvement of the PTEN/AKT signaling pathway was examined using the PTEN inhibitor SF1670.

Results: NUP155 was downregulated in postexercise samples and upregulated in NSCLC samples, indicating its association with poor prognosis in NSCLC. Knockdown of NUP155 in NSCLC cell lines resulted in reduced cell viability, migration, and invasion, alongside increased apoptosis. Western blotting revealed that NUP155 knockdown upregulated PTEN levels and downregulated phosphorylated AKT (p-AKT), without altering total AKT levels. The addition of SF1670 partially reversed the effects of NUP155 knockdown, indicating the involvement of the signaling pathway PTEN/AKT in NUP155-mediated tumorigenesis.

Conclusions: NUP155 is upregulated in NSCLC, which promotes cell invasion and migration via the PTEN/AKT signaling pathway. Targeting NUP155, potentially influenced by exercise, could be a promising therapy. Combining exercise with targeted treatments may enhance patient outcomes.

Background: OpenAI's ChatGPT is a large language model-based artificial intelligence (AI) chatbot that can be used to answer unique, user-generated questions without direct training on specific content. Large language models have significant potential in urologic education. We reviewed the primary data surrounding the use of large language models in urology. We also reported findings of our primary study assessing the performance of ChatGPT in renal cell carcinoma (RCC) education.

Methods: For our primary study, we utilized three professional society guidelines addressing RCC to generate fifteen content questions. These questions were inputted into ChatGPT 3.5. ChatGPT responses along with pre- and post-content assessment questions regarding ChatGPT were then presented to evaluators. Evaluators consisted of four urologic oncologists and four non-clinical staff members. Medline was reviewed for additional studies pertaining to the use of ChatGPT in urologic education.

Results: We found that all assessors rated ChatGPT highly on the accuracy and usefulness of information provided with overall mean scores of 3.64 [±0.62 standard deviation (SD)] and 3.58 (±0.75) out of 5, respectively. Clinicians and non-clinicians did not differ in their scoring of responses (P=0.37). Completing content assessment improved confidence in the accuracy of ChatGPT's information (P=0.01) and increased agreement that it should be used for medical education (P=0.007). Attitudes towards use for patient education did not change (P=0.30). We also review the current state of the literature regarding ChatGPT use for patient and trainee education and discuss future steps towards optimization.

Conclusions: ChatGPT has significant potential utility in medical education if it can continue to provide accurate and useful information. We have found it to be a useful adjunct to expert human guidance both for medical trainee and, less so, for patient education. Further work is needed to validate ChatGPT before widespread adoption.

Background: Prior prospective studies have demonstrated the efficacy of poly(adenosine diphosphate-ribose) polymerase inhibitors (PARPis) in various cancers with mutations in the breast cancer gene (BRCA), such as ovarian and breast cancers. However, PARPi have also been associated with an increased incidence of therapy-related myeloid neoplasms (t-MNs). This study aimed to investigate the incidence of t-MNs following PARPi therapy in patients with ovarian or primary peritoneal cancer in Korea and to identify related risk factors.

Methods: We retrospectively analyzed data of patients with ovarian or primary peritoneal cancer who received PARPi therapy between January 2015 and June 2023.

Results: Among 52 patients treated with PARPi, four were diagnosed with t-MNs. All four patients had BRCA mutations, and two of them had breast cancer with no evidence of disease (NED) status following treatment. All patients received radiotherapy and at least one granulocyte-colony stimulating factor (G-CSF) application. The median duration of PARPi therapy was 16.3 (range, 6.2-48.8) months. At the time of analysis, three patients had metastatic ovarian cancer and one maintained the NED status. Next-generation sequencing (NGS) performed in four patients revealed TP53 mutations and complex karyotypes in all tested patients. Among the four patients, three received only supportive care, and one was actively undergoing t-MN treatment.

Conclusions: The incidence of t-MNs after PARPi therapy in the current study was higher than that of overall t-MNs, which is consistent with the results of previous studies on t-MNs after PARPi therapy. Further international studies are needed to elucidate the mechanism and clinical characteristics of t-MNs associated with PARPi therapy.

Background: Immunogenic cell death (ICD) has been verified as a modality of regulated cell death (RCD). Bladder cancer (BC) is a common malignant tumor and ranks tenth in the incidence of global tumor epidemiology. We conducted this study to understand the relationship between ICD and BC and benefit clinical practice.

Methods: Transcriptome and clinical profiling, mutational data of patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. BC patients were divided into ICD-high and -low risk subgroups via consensus clusters. Functional enrichment, somatic mutation analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore the potential mechanism. An ICD-related risk signature was constructed via least absolute shrinkage and selection operator (LASSO) regression analysis. Immune infiltration was investigated and multiplexed immunofluorescence staining was used to validate the BC microenvironment. Immune landscape was summarized to show the potential of immunotherapy.

Results: A total of 18 differentially expressed ICD-related genes in BC were distinguished from normal tissue. We identified two clusters and BC patients were divided into ICD-high and -low subgroups in the TCGA BC cohort. The ICD-high subgroup exhibited worse clinical outcomes, different mutation profiles, different functional enrichment, higher immune infiltration, and better immunotherapy response. An ICD-related risk signature made of seven ICD-related genes was established and shown to have outstanding predictive power of prognosis via LASSO Cox regression.

Conclusions: An ICD-related risk signature was established that provides a promising classification system to predict the prognosis in BC patients accurately. The signature provides a novel strategy for immunotherapy of BC.

Background: Characterized by its high mortality and easy recurrence, hepatocellular carcinoma (HCC) poses significant clinical challenges. The association between copper metabolism and development of cancer has been identified. However, the underlying mechanisms of copper metabolism-related long non-coding RNAs (CMRLs) in HCC remain elusive. To address the gap, our study analyzed the prognostic and immuno-therapeutic value of CMRLs in HCC.

Methods: This research utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data (n=424) for analysis, applying the "limma" package in R software for differential gene analysis and construction of a prognostic signature. We validated the signature using training and validation groups stochastically divided at a ratio of 1:1 and assessed prognostic value via Kaplan-Meier, C-index, and receiver operating characteristic (ROC) curves. By multivariate Cox regression, independent prognostic indicators were identified, and a nomogram was formulated for survival forecasting. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses elucidated biological pathways, and the immune landscape was examined through multiple algorithms. Finally, drug sensitivity was determined from Genomics of Drug Sensitivity in Cancer (GDSC), with mutation analysis conducted via maftools.

Results: In this study, a predictive model based on four pivotal CMRLs (PRRT3-AS1, AC108752.1, AC092115.3, AL031985.3) significantly associated with HCC progression and prognosis was constructed and validated with the overall survival (OS) prediction area under the curve (AUC) values for 1, 3, and 5 years of 0.718, 0.688, and 0.669, respectively. The calibration curves and C-index values showed a solid prognostic ability of the nomogram. The high-risk group was notably higher than the low-risk group both in OS and tumor mutational burdens (TMBs). Moreover, functional annotation enrichment analysis of CMRLs revealed that the signature was mainly associated with mitotic function, chromosome, kinetochore, cell cycle, and oocyte meiosis. Furthermore, therapeutic drugs, including fluorouracil, afatinib, alpelisib, cedranib, crizotinib, erlotinib, gefitinib, and ipatasertib, were found to induce higher sensitivity in high-risk group.

Conclusions: The prognostic signature consisting of four CMRLs displays an outstanding predictive performance and improves the precision of immuno-oncology.

Background: Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.

Methods: Employing a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes. A prognostic model was developed using the least absolute shrinkage and selection operator (LASSO) regression and independently validated against similarly composed HGSOC datasets. Comprehensive gene expression and immune cell infiltration analyses were conducted, employing advanced algorithms like xCell to delineate the immune landscape of HGSOC.

Results: Our investigation unveiled distinctive immune cell infiltration patterns and gene expression profiles within the TME of HGSOC. Notably, the prevalence of exhausted CD8⁺ T cells in high-risk patient samples emerged as a critical finding, underscoring the dualistic nature of the immune response in OC. The developed prognostic model, incorporating immune cell markers, exhibited robust predictive accuracy for patient outcomes, showing significant correlations with immunotherapy responses and drug sensitivities.

Conclusions: This study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.

Background: Glioblastoma (GBM) is a frequent malignant tumor in neurosurgery characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficiency (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is unknown, however, whether the molecular characteristics linked with HRD have a predictive role in GBM. The study aims to assess the extent of genomic instability in GBM using HRD score and investigate the prognostic significance of HRD-related molecular features in GBM.

Methods: The discovery cohort comprised 567 GBM patients from The Cancer Genome Atlas (TCGA) database. We established HRD scores using the single nucleotide polymorphism (SNP) array data and analyzed transcriptomic data from patients with different HRD scores to identify biomarkers associated with HRD. A prognostic model was built by using HRD-related differentially expressed genes (DEGs) and validated in a distinct cohort from the Chinese Glioma Genome Atlas (CGGA) database.

Results: Based on the SNP array data, the gene expression profile data, and the clinical characteristics of GBM patients, we found that patients with a high HRD score had a better prognosis than those with a low HRD score. The DNA damage repair (DDR) signaling pathways were notably enriched in the HRD-positive subgroup. The prognostic model was developed by including HRD-related DEGs that could evaluate the clinical prognosis of patients more efficiently than the HRD score. In addition, patients with a low-risk score had a considerably augmented signature of γδT cells. Finally, through univariate and multivariate Cox regression analyses, it was demonstrated that the prognostic model was superior to other prognostic markers.

Conclusions: In conclusion, our research has not only demonstrated that a high HRD score is a valid prognostic biomarker in GBM patients but also built a stable prognosis model [odds ratio (OR) 0.18, 95% confidence interval (CI): 0.11-0.23, P<0.001] that is more accurate than conventional prognostic markers such as O6-methylguanine-DNA methyltransferase (MGMT) methylation (OR 0.55, 95% CI: 0.33-0.91, P=0.02).

Background: Tumor suppressors are well known drivers of cancer invasion and metastasis in metastatic castration sensitive prostate cancer (mCSPC). However, oncogenes are also known to be altered in this state, however the frequency and prognosis of these alterations are unclear. Thus, we aimed to study the spectrum of oncogene mutations in mCSPC and study the significance of these alteration on outcomes.

Methods: Four hundred and seventy-seven patients with mCSPC were included who underwent next generation sequencing. Oncogene alterations were defined as mutations in ALK, AKT1-3, BRAF, CCND1-3, CTNNB1, EGFR, ERBB2, FGFR1, FGFR2, HRAS, KRAS, MDM2, MET, MITF, MYC, NOTCH1-3, NRAS, PIK3CA, PI3KCB, PIK3R1, RET. Endpoints of interests were radiographic progression-free survival (rPFS), time to development of CRPC (tdCRPC), and overall survival (OS). Kaplan Meier analysis was performed and Cox regression hazard ratios (HR) calculated.

Results: A total of 477 patients were included with baseline characteristics with 117 patients (24.5%) harbored a mutation within an oncogene. A total of 172 oncogene mutations were found within the population with the most common being MYC (n=29; 16.9%), PIK3CA (n=24; 14%), CTNNB1 (n=22, 12.8%), BRAF (n=10, 5.8%), and CCND1 (n=10, 5.8%). Oncogene mutations were associated with inferior rPFS (19.2 vs. 32.2 months, P=0.03), tdCRPC (15.7 vs. 32.4 months, P<0.001), and OS (5-year OS 75.3% vs. 55.4%, P=0.01). On multivariable analysis oncogene mutations were strongly associated with tdCRPC (HR 1.42, P=0.03).

Conclusions: Oncogenes are frequency mutated in mCSPC and associated with aggressive features and inferior outcomes. Future work will need to validate these results to better assess its significance in allowing for personalization of care.