Discover. Oncology最新文献

Background: Immunotherapy has revolutionized treatment for advanced gastric/gastroesophageal junction adenocarcinoma (G/GEJC). However, identifying convenient biomarkers for predicting therapeutic efficacy remains challenging. This study investigated the association between clinicopathological characteristics and baseline peripheral blood lymphocyte subsets with efficacy of first-line chemotherapy combined with immune checkpoint inhibitors (ICIs) in proficient mismatch repair (pMMR) human epidermal growth factor receptor 2 (HER2)-negative advanced G/GEJC.

Methods: This retrospective study enrolled 97 patients with pMMR HER2-negative advanced G/GEJC receiving first-line chemotherapy combined with ICIs. Clinicopathological characteristics and peripheral blood lymphocyte subsets were collected. Overall survival (OS) and progression-free survival (PFS) were used to evaluate efficacy. The univariate and multivariate analyses were conducted using Cox regression analysis.

Results: Median PFS and OS were 5.9 and 15.2 months, respectively. Tumor location, Lauren classification, tumor differentiation, peritoneal metastases, neutrophil to lymphocyte ratio (NLR), and regulatory T cells (Tregs) as significantly associated with PFS. Well-differentiated tumor and higher Tregs independently predicted longer PFS. For OS, only higher NLR was an independent risk factor. Optimal cut-offs for NLR (3.5) and Tregs (10.1) stratified patients with significantly different PFS. A nomogram combining Tregs, NLR, peritoneal metastases, and tumor differentiation achieved superior predictive performance compared to PD-L1 CPS alone, with PFS AUC of 0.68-0.77 and OS AUC of 0.69-0.75.

Conclusions: Clinicopathological characteristics and baseline peripheral lymphocyte subsets were significantly associated with efficacy of first-line chemotherapy combined with ICIs in pMMR HER2-negative advanced G/GEJC, highlighting the potential utility of integrating these accessible parameters for efficacy prediction.

Background: Lymphomas in children and adolescents accounted for about 25% of all lymphoma cases. The cancer-specific death (CSD) of children and adolescent-onset remains dismal and varies widely in different individuals. The current investigation aimed to develop a predicting nomogram to evaluate CSD risk in children and adolescents‑onset lymphoma with data from the Surveillance, Epidemiology and End Results (SEER) database.

Methods: The pathological variables of lymphomas patients were extracted from the SEER database. A competing risk model was used to identify independent factors for CSD. The predicting nomogram was also developed. The performance of the nomogram was evaluated with calibration curves, receiver operating characteristic curve and decision curve.

Results: A total of 7349 lymphoma cases were selected in our investigation, which were separated into the training (n = 5144) and testing (n = 2205) cohorts. The results of univariate and multivariate analysis demonstrated age, race, year of diagnosis, pathological subtype, tumor grade, tumor stage, and chemotherapy as independent risk factors for CSD in lymphoma cases. The 1-year, 3-year-, and 5-year AUCs of ROC curves of nomogram for CSD in lymphoma were 0.855, 0.827, and 0.810 in the training cohort and 0.862, 0.829, and 0.812 in the testing cohort, respectively. Further analysis suggested a good agreement between the observed outcome and the predicted probabilities in the calibration curves in training cohort and testing cohort. Moreover, decision curve analysis also indicted good clinical utility of the nomogram models in training cohort and validation cohort.

Conclusion: The nomogram shows good accuracy and reliability in evaluating the risk of CSD in children and adolescents‑onset lymphoma, and it could provide some theoretical support for clinicians to make decisions.

Hepatocellular carcinoma (HCC) represents an extremely complex and heterogeneous malignant tumor. Protein palmitoylation, a highly conserved and pivotal form of post-translational protein modification, is extensively implicated in cancer progression and exerts a regulatory role in immune responses. Nevertheless, the prognosis significance and therapeutic potential of palmitoylation-related genes (PRGs) in HCC remain incompletely explored. In the present study, we systematically analyzed multiple transcriptome cohort samples from the TCGA, ICGC and GEO databases, including the TCGA-LIHC, ICGC-LIRI, GSE16757, GSE54236, GSE14520, GSE45267 and GSE36376 cohorts. Subsequently, within the TCGA training cohort, a PRGsSig comprising three hub PRGs, namely HSP90AA1, CTHRC1, and PTDSS2, was constructed via machine learning algorithms. Then, the efficacy of this PRGsSig on prognosis prediction was assessed in the training and validation cohorts. Further analysis of immunotherapy response indicated that patients with low PRGsSig scores benefited more from treatment. Additionally, remarkable disparities were observed between patients in different signature score groups in terms of clinical characteristics, tumor mutation burden, tumor microenvironment, and potential drugs. Furthermore, among the three hub PRGs, PTDSS2 was significantly upregulated in HCC cells and its knockdown significantly inhibited the proliferation and metastasis of HCC cells. In conclusion, we established a robust PRGsSig that offers valuable insights for prognostic prediction and informs treatment strategies in HCC.

Background: Glioblastoma (GBM), as a high-grade glioma, has high invasiveness and poor clinical prognosis. Manganese is an important trace element, has been proven to be closely related to tumor treatment and tumor immunity. It is necessary to explore the correlation between manganese metabolism-related genes and GBM.

Methods: We downloaded RNA gene expression data and clinical data of GBM patients from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas database (CGGA) databases. Build the signature using data from TCGA-GBM and independently validate it using data from CGGA-GBM. Next, we will use a nomogram to predict the clinical prognosis of GBM patients. Finally, we analyzed the relationship between the prognostic model and immune microenvironment through CIBERSORT.

Results: A total of 495 manganese metabolism-related differentially expressed genes were obtained for the establishment of a subsequent signature in the TCGA-GBM cohort. The following seven genes (PLAT, TIMP1, FN1, CTSB, SCG5, GALNT6 and AMPH) were used to establish the signature and independently validated using the CGGA-GBM dataset. Research has confirmed that the predictive ability of this signature exceeds other clinical features, and the receiver operating characteristic curve has a high area under the curve.

Conclusions: We constructed and validated a novel gene signature related to manganese metabolism in GBM patients. This gene signature not only reliably predicts the clinical outcomes of GBM patients but also has the potential to guide the provision of new treatment options for these patients.

Backgrounds: Non-small cell lung cancer (NSCLC) is an aggressive malignant tumor characterized by early recurrence and poor prognosis. Homotypic cell-in-cell (HoCIC) are significantly associated with adverse outcomes in multiple tumors, serving as valuable indicators for patient outcome assessment. However, current HoCIC diagnosis methods rely primarily on manual microscopic observation and lack standardized detection biomarkers and methodologies, which may introduce bias into research findings. Therefore, this study aims to identify diagnostic markers for HoCIC in NSCLC, laying the foundation for further research into the biological roles and mechanisms of HoCIC.

Methods: Kaplan‒Meier curves and log-rank tests were used to investigate the relationship between HoCIC and prognosis. Bioinformatics analysis of NSCLC gene expression data related to HoCIC from the Gene Expression Omnibus (GEO) dataset revealed differentially HoCIC expressed genes ( HoCICDEGs) between tumor tissues and normal tissues. We identified an overlapping HoCIC hub gene, BECN1, among the HoCICDEGs and autophagy-related genes (ARGs). The expression and biological functions of BECN1 were analysed via The Cancer Genome Atlas (TCGA) database. The Kaplan‒Meier, TIMER2.0, cBioPortal, and GSCA public databases were subsequently used to investigate the prognosis, immune infiltration, genetic alterations, and drug sensitivity associated with BECN1. Finally, clinical NSCLC samples were collected for immunohistochemical experiments to validate BECN1 expression and its diagnostic value for HoCIC.

Results: HoCIC was significantly correlated with poor overall survival (OS) and disease-free survival (DFS). We identified BECN1 as a core gene associated with HoCIC in NSCLC, which is highly expressed in tumor tissues and is correlated with unfavourable prognosis. BECN1 is correlated with the mitotic spindle, G2M checkpoint, and MYC pathways, suppresses immune cell infiltration, and is sensitive to most anticancer drugs. In our validated NSCLC cohort, BECN1 protein was highly expressed in tumor tissues and demonstrated a significant association with HoCIC, serving as an independent risk factor for HoCIC. The HoCIC prediction model constructed on the basis of BECN1 demonstrated favourable diagnostic capability, discriminatory power, and clinical benefit.

Conclusions: In summary, this study identified BECN1 as a diagnostic biomarker associated with HoCIC in NSCLC, providing a strong foundation for improving diagnostic and research strategies related to this phenomenon.