Clinical Medicine Insights-Oncology最新文献

Background: To investigate the impact of latent tuberculosis infection (LTBI) on the prognosis of non-small cell lung cancer (NSCLC) patients treated with anti-PD-1 immunotherapy and to assess the correlation between dynamic alterations in T-SPOT.TB results and prognosis.

Methods: This retrospective cohort study analyzed clinical data from 127 patients with NSCLC who received anti-PD-1 therapy and underwent T-SPOT.TB testing at our institution between January 2020 and March 2024. Baseline imbalances between groups were addressed using inverse probability of treatment weighting (IPTW). Restricted cubic spline (RCS) modeling, Cox regression and other analyses were conducted both before and after IPTW.

Results: Among the entire cohort, 50 patients were in the LTBI group and 77 in the Normal group. No significant differences were observed in mPFS or mOS between the two groups. RCS analysis revealed a nonlinear (U-shaped) relationship between pre-TSPOT values and OS. Patients with a T-SPOT positive (but value ⩽18) exhibited longer OS compared with the other two groups (HR = 0.13, 95% CI [0.03 ~ 0.54], P = .005; after IPTW HR = 0.21, 95% CI [0.05-0.90], P = .035). Among 63 patients monitored for dynamic TSPOT changes, 35 (55.56%) remained persistently negative, 15 (23.81%) remained persistently positive, 2 (3.17%) converted from negative to positive, and 11 (17.46%) converted from positive to negative. No significant differences in ORR, PFS, or OS across these groups.

Conclusions: Although no statistically significant differences in treatment efficacy and prognosis were observed between the LTBI and Normal groups, this finding should not be interpreted as therapeutic equivalence, particularly given the limited sample size. Pre-treatment T-SPOT values showed a nonlinear (U-shaped) relationship with patient prognosis (OS). Lower pre-treatment T-SPOT value were associated with longer OS. The dynamic changes in T-SPOT during treatment were not significantly associated with outcomes. Four patients developed active tuberculosis during immunotherapy, with heterogeneous T-SPOT patterns, underscoring the need for TB monitoring in ICI-treated patients.

Background: Programmed cell death 1 (PDCD1) is an immune checkpoint inhibitor that plays an important role in immune evasion in breast cancer (BC). In this study, we aimed to evaluate the correlation between PDCD1 expression, immune cell tumor infiltration, and prognosis. In addition, we also developed a predictive model to determine PDCD1 expression levels in patients with BC based on radiomics features extracted from magnetic resonance imaging (MRI).

Methods: Clinical data of 1082 patients with BC extracted from The Cancer Genome Atlas (TCGA) and MRI data of 108 patients with BC extracted from The Cancer Imaging Archive (TCIA) were used to determine the correlation between PDCD1 expression levels and the prognosis, clinical stage, survival, and levels of immune cell tumor infiltration in patients with BC. Predictive radiomics features for PDCD1 were extracted by 2 physicians from MRI data. The top 5 predictive features were evaluated and selected to build 2 machine learning models.

Results: The PDCD1 expression levels were significantly higher in tumor tissues from patients with BC (P < .001). High PDCD1 expression levels were associated with improved overall survival, hazard ratio (HR) = 0.63, 95% confidence interval (CI) 0.425-0.934, P = .021. The PDCD1 expression levels showed a significant positive correlation with immune cell infiltration, including CD8 (P < .001) and Treg (P < .001). Both MRI radiomics models demonstrated good accuracy, strong clinical utility, and a high level of consistency in discriminating between low and high PDCD1 expression levels (P > .05).

Conclusions: PDCD1 expression showed a good correlation with prognosis and tumor immune cell infiltration. The MRI radiomics model accurately predicted PDCD1 expression levels and could potentially serve as a noninvasive tool to predict early tumor response to immunotherapy.

Background: Lung adenocarcinoma (LUAD) is the most common lung cancer, associated with high metastasis and low survival rates. Identifying reliable biomarkers is essential for better prognosis and treatment.

Methods: In this study, we analyzed RNA sequencing data, mutation information, and clinical data from the TCGA-LUAD cohort and other multicenter datasets to investigate the role of Holliday junction recognition protein (HJURP) in LUAD. We employed immunohistochemistry in tissue microarray cohort to validate the prognostic significance of HJURP. The DepMap project was used to validate the effect of HJURP knockout in vitro.

Results: Holliday junction recognition protein was identified as an adverse prognostic factor in the TCGA-LUAD cohort and diverse ethnic groups. Its expression correlated with poor immunotherapy outcomes, and HJURP knockout suppressed cancer cell proliferation. High HJURP expression was linked to increased mutation frequency, particularly in TP53 and TTN. Pan-cancer analysis also indicated HJURP as a poor prognostic factor in various solid tumors.

Conclusions: Holliday junction recognition protein emerges as a significant biomarker in LUAD, consistently associated with poor prognosis across multiple cohorts. Its role in various oncogenic pathways and correlation with advanced disease stages underscore the potential of HJURP as a target for therapeutic intervention and as a marker for prognosis in LUAD.

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

Currently, one of the most dynamic and rapidly advancing areas in biomedical research is the study of cell signaling systems. In particular, researchers have directed significant attention toward the Wnt signaling pathway, which has emerged as a critical player in several biological processes, including embryonic development, cancer progression, and the maintenance of tissue homeostasis. The growing body of research demonstrating the Wnt pathway's critical functions in various activities emphasizes the pathway's importance. Lymphoid enhancer factor-1 (LEF-1) is a crucial component of the Wnt signaling cascade, among its numerous components. The β-catenin/LEF complex, which is essential for triggering transcriptional responses, is formed when the N-terminal domain of LEF-1 binds with β-catenin. This complex acts as a central "activation hub" within the Wnt pathway, integrating signals from β-catenin and LEF-1 to facilitate gene expression that is critical for cellular functions. This narrative review focuses on highlighting the latest advancements in LEF-1 research, particularly its role in cancer. By emphasizing the significance of LEF-1 in the processes of carcinogenesis, the discussion aims to shed light on the potential implications of these findings for developing innovative treatment strategies. Understanding the function of LEF-1 not only enhances our comprehension of tumor biology but also opens pathways to novel therapeutic interventions.

Background: Malignant peritoneal mesothelioma (MPeM) is a rare and progressive cancer originating from the mesothelial cells of the peritoneum. In patients with early-stage disease who are suitable for surgery, the treatment of choice is CRS + HIPEC, whereas in advanced-stage patients, systemic treatments are applied. Pemetrexed plus platinum regimens are at the forefront of first-line systemic treatments. Gemcitabine plus platinum regimens are rarely used as first-line treatment for MPeM. The aim of our study is to compare the efficacy of first-line pemetrexed plus platinum with gemcitabine plus platinum regimens in patients with MPeM.

Methods: In this study, a retrospective analysis was conducted on 48 patients with MPeM who were followed up in our clinic between 2001 and 2025. In our study, 28 patients received pemetrexed plus platinum as a first-line regimen, while 20 patients received gemcitabine plus platinum. The median overall survival (OS), median progression-free survival (PFS), and response rates for both regimens were analyzed. In addition, prognostic factors influencing overall survival were investigated in the entire patient population.

Results: The median PFS and OS were 11.1 months and 17.0 months for pemetrexed and 8.01 months and 14.4 months for gemcitabine. Although pemetrexed showed numerically higher PFS and OS, the difference was not statistically significant. The objective response rate (ORR) and disease control rate (DCR) were 32.1% and 57.1% for pemetrexed, compared with 25% and 40% for gemcitabine, showing pemetrexed's superiority in response rates. In the entire patient population, CRS + HIPEC was the main prognostic factor for survival.

Conclusion: We have demonstrated that the pemetrexed + platinum regimen has better response rates compared with the gemcitabine + platinum regimen in MPeM patients. However, gemcitabine-based regimens can be used as an alternative to pemetrexed in patients with MPeM.

Background: This study aimed to examine severe bone marrow suppression risk factors during radiotherapy in patients with cervical cancer and to develop and validate a visual evaluation tool for predicting the risk of severe bone marrow suppression during radiotherapy in these patients.

Methods: A total of 300 patients with cervical cancer who underwent radiotherapy were retrospectively included in this cohort study. Patients were randomly divided into a model group (n = 240) and a validation group (n = 60) at a ratio of 8:2. Univariate and multivariate logistic regression analyses were performed to explore and establish a nomogram prediction model. The feasibility of this nomogram model in predicting the risk of severe bone marrow suppression during radiotherapy in patients with cervical cancer was assessed in the validation cohort. The discrimination ability, accuracy, and clinical utility of the model were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: Menopausal status, Karnofsky performance score (KPS), clinical stage, concurrent chemotherapy status, and pre-radiotherapy creatinine level were identified as independent risk factors for severe bone marrow suppression during radiotherapy in patients (P < .05). DCA revealed that the nomogram model had a greater net benefit in predicting the risk of severe bone marrow suppression during radiotherapy in patients with cervical cancer when the patient's threshold probability was between 0.20 and 0.93.

Conclusion: The nomogram model based on these independent risk factors exhibited good predictive performance, assisting in individualized risk assessment and facilitating early intervention to benefit patients during radiotherapy.

Background: Vietnam is an endemic region for nasopharyngeal cancer (NPC), with approximately 70% of cases diagnosed at stage III-IVa and 90% presenting with undifferentiated histology closely related to Epstein-Barr virus (EBV) infection. However, no studies have identified biomarkers predictive of recurrence and metastasis. This study aimed to determine the cut-off concentrations of cell-free (cf) EBV DNA for predicting recurrence and metastasis in NPC.

Methods: A longitudinal descriptive study was conducted on 58 patients with stage III-IVa undifferentiated NPC between August 2021 and August 2024. We analysed the predictive value of cf EBV DNA concentrations at pre-treatment (preEBV) and post-treatment (postEBV, 6 months postEBV [EBV6], 12 months postEBV [EBV12]) in relation to disease progression and survival outcomes.

Results: Of the 58 patients, 23 experienced recurrence and/or metastasis. The recurrence prediction cut-off concentration was 3980.0 copies/mL for preEBV (area under the curve [AUC] = 0.705, 95% confidence interval [CI]: 0.499-0.911, Se = 80.0%, Sp = 64.6%, odds ratio [OR] = 7.294, 95% CI: 1.389-38.307, accuracy [ACC] = 67.2%); 12.5 copies/mL for EBV6 (AUC = 0.783, 95% CI: 0.600-0.966, Se = 77.8%, Sp = 75.0%, OR = 10.5, 95% CI: 1.839-54.242, ACC = 75.5%); and 5.5 copies/mL for EBV12 (AUC = 0.766, 95% CI: 0.577-0.954, Se = 83.3%, Sp = 78.0%, OR = 17.778, 95% CI: 1.835-172.219, ACC = 78.7%). The metastasis prediction cut-off concentration for EBV12 was 42.5 copies/mL (AUC = 0.748, 95% CI: 0.502-0.994, Se = 66.7%, Sp = 80.5%, OR = 8.250, 95% CI: 1.278-52.254, ACC = 78.7%). Cut-off concentrations of preEBV, EBV6, and EBV12 were independent predictors of survival outcomes (except for EBV6). All results were statistically significant (P < .05).

Conclusions: In advanced-stage NPC-undifferentiated subtype, preEBV, EBV6, and EBV12 serve as predictive biomarkers for recurrence and metastasis. Among these, EBV12 demonstrated the highest predictive value.

Background: Myelosuppression is a frequent complication in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy. Current clinical practice relies predominantly on treatment-phase monitoring for myelosuppression risk assessment, while effective pretreatment prediction tools are lacking. This study developed a predictive model based on pretreatment clinical indicators to facilitate early identification of high-risk patients and support clinical decision-making.

Methods: We conducted a retrospective cohort study using electronic medical records of 210 patients with NPC who received chemoradiotherapy at the First Affiliated Hospital of Bengbu Medical University between May 2016 and December 2021. Using R software, patients were randomly allocated into a training set (n = 150) and an internal validation set (n = 60) at a 7:3 ratio. Variable selection was performed using Least Absolute Shrinkage and Selection Operator regression, followed by univariable and multivariable logistic regression analyses to identify potential predictors. Following categorization of these identified potential predictors, Firth penalized-likelihood regression was employed to correct for small-sample bias, while multicollinearity was rigorously assessed using variance inflation factors (VIFs). A predictive nomogram was subsequently constructed. Model performance was evaluated through multiple validation metrics, including the concordance index (C-index), receiver operating characteristic curve analysis, clinical decision curve analysis, and calibration curve.

Results: Multivariable logistic regression analysis identified 3 potential predictors of myelosuppression: pretreatment plateletcrit (PCT), direct bilirubin (DBIL), and sodium ions (Na⁺) (all P < .05). All these potential predictors met strict stability criteria after conversion to categorical variables (all VIF < 2.1, with a predefined threshold of VIF < 5). Model evaluation demonstrates that the developed nomogram exhibits favorable predictive performance.

Conclusion: Pretreatment PCT, DBIL, and Na⁺ may serve as potential predictors of myelosuppression in patients with NPC undergoing chemoradiotherapy. This nomogram could serve as a risk stratification tool to identify high-risk patients before treatment, enabling early interventions for myelosuppression prevention.