Significant Predictive Value For Overall Survival Research Articles

Deciphering potential molecular mechanisms in clear cell renal cell carcinoma based on the ubiquitin-conjugating enzyme E2 related genes: Identifying UBE2C correlates to infiltration of regulatory T cells.

Renal clear cell carcinoma (ccRCC) is a highly aggressive and common form of kidney cancer, with limited treatment options for advanced stages. Recent studies have highlighted the importance of the ubiquitin-proteasome system in tumor progression, particularly the role of ubiquitin-conjugating enzyme E2 (UBE2) family members. However, the prognostic significance of UBE2-related genes (UBE2RGs) in ccRCC remains unclear. In this study, bulk RNA-sequencing and single-cell RNA-sequencing data from ccRCC patients were retrieved from the Cancer Genome Atlas and Gene Expression Omnibus databases. Differential expression analysis was performed to identify UBE2RGs associated with ccRCC. A combination of 10 machine learning methods was applied to develop an optimal prognostic model, and its predictive performance was evaluated using area under the curve (AUC) values for 1-, 3-, and 5-year overall survival (OS) in both training and validation cohorts. Functional enrichment analyses of gene ontology and Kyoto Encyclopedia of Genes and Genomes were conducted to explore the biological pathways involved. Correlation analysis was conducted to investigate the association between the risk score and tumor mutational burden (TMB) and immune cell infiltration. Immunotherapy and chemotherapy sensitivity were assessed by immunophenoscore and tumor immune, dysfunction, and exclusion scores to identify potential predictive significance. In vitro, knockdown of the key gene UBE2C in 786-O cells by specific small interfering RNA to validate its impact on apoptosis, migration, cell cycle, migration, invasion of tumor cells, and induction of regulatory T cells (Tregs). Analysis of sc-RNA revealed that UBE2 activity was significantly upregulated in malignant cells, suggesting its role in tumor progression. A three-gene prognostic model comprising UBE2C, UBE2D3, and UBE2T was constructed by Lasoo Cox regression and demonstrated robust predictive accuracy, with AUC values of 0.745, 0.766, and 0.771 for 1-, 3-, and 5-year survival, respectively. The model was validated as an independent prognostic factor in ccRCC. Patients in the high-risk group had a worse prognosis, higher TMB scores, and low responsiveness to immunotherapy. Additionally, immune infiltration and chemotherapy sensitivity analyses revealed that UBE2RGs are associated with various immune cells and drugs, suggesting that UBE2RGs could be a potential therapeutic target for ccRCC. In vitro experiments confirmed that the reduction of UBE2C led to an increase in apoptosis rate, as well as a decrease in tumor cell invasion and metastasis abilities. Additionally, si-UBE2C cells reduced the release of the cytokine Transforming Growth Factor-beta 1 (TGF-β1), leading to a decreased ratio of Tregs in the co-culture system. This study presents a novel three-gene prognostic model based on UBE2RGs that demonstrates significant predictive value for OS, immunotherapy, and chemotherapy in ccRCC patients. The findings underscore the potential of UBE2 family members as biomarkers and therapeutic targets in ccRCC, warranting further investigation in prospective clinical trials.

Clinical Impact of Nutritional Status and Sarcopenia in Pediatric Patients with Bone and Soft Tissue Sarcomas: A Pilot Retrospective Study (SarcoPed).

Background: We evaluated nutritional and sarcopenia status and their clinical impact in pediatric patients affected by bone and soft tissue sarcomas. Methods: Body mass index (BMI), prognostic nutritional index (PNI), and total psoas muscle area (tPMA) at diagnosis and after 12 months were analyzed. tPMA was measured from single cross-sectional computed tomography (CT) images at L4–L5. Age-specific and sex-specific tPMA Z-scores were retrieved from an online calculator. Results: A total of 21 patients were identified between February 2013 and December 2018. Twelve patients (57.1%) experienced sarcopenia at diagnosis, although not statistically associated with overall survival (OS) (p = 0.09). BMI Z-score, PNI, and tPMA Z-score significantly decreased between diagnosis and after 12 months of treatment (p < 0.05). Univariate analysis showed significant associations between poor OS and the presence of metastasis (p = 0.008), the absence of surgery (p = 0.005), PNI decrease (p = 0.027), and the reduction in tPMA > 25% (p = 0.042) over the 12 months. Conclusions: Sarcopenia affects more than half of the patients at diagnosis. Decreased PNI during 12 months of treatment has significant predictive value for OS. The role of tPMA derived from CT scan among pediatric patients with sarcoma should be investigated in further prospective and larger studies.

Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study

Purpose This study aimed to investigate the application of the number of positive lymph nodes (PLNs) in tumor, node, metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) patients. Patients and Methods. We screened a total of 15820 patients with resected NSCLC between 2004 and 2015 from SEER database. The X-tile model was used to determine the cutoff values of the number of PLNs. Overall survival (OS) curves were plotted using the Kaplan–Meier method, and the differences among the individual groups were defined using the log-rank test. Cox regression model was used to perform univariate and multivariate analyses and to assess the association between the number of PLNs and OS. Results In this study, using the X-tile model, we screened three different cutoff values, including nN0, nN1–3, and nN4-. Survival curves demonstrated that our defined nN stage had a significant predictive value for OS (P < 0.001). In the univariate and multivariate Cox analyses, the result showed that nN stage was a significant prognostic factor of OS for NSCLC patients (P < 0.001). Subsequently, we classified the patients into five subgroups based on the combination of pN and nN stages, including pN0 + nN0, pN1 + nN1-3, pN2 + nN1-3, pN1 + nN4-, and pN2 + nN4-. Moreover, survival curves revealed significant differences among these five groups (P < 0.001). Conclusion A combination of pathological LNs (pN) and the number of LN (nN) involvement in NSCLC patients had a better prognostic value than the current TNM staging system based on only pN stage.

Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma.

BackgroundBoth histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC).MethodsA dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training (n = 108) or validation (n = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set.ResultsIn validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference (HR = 10.66, 95%CI: 5.06–26.8, p < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model.ConclusionThe histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.

Can adaptive threshold-based metabolic tumor volume (MTV) and lean body mass corrected standard uptake value (SUL) predict prognosis in head and neck cancer patients treated with definitive radiotherapy/chemoradiotherapy?

PurposeTo evaluate the predictive value of adaptive threshold-based metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and maximum lean body mass corrected SUV (SULmax) measured on pretreatment positron emission tomography and computed tomography (PET/CT) imaging in head and neck cancer patients treated with definitive radiotherapy/chemoradiotherapy. Materials and methodsPretreatment PET/CT of the 62 patients with locally advanced head and neck cancer who were treated consecutively between May 2010 and February 2013 were reviewed retrospectively. The maximum FDG uptake of the primary tumor was defined according to SUVmax and SULmax. Multiple threshold levels between 60% and 10% of the SUVmax and SULmax were tested with intervals of 5% to 10% in order to define the most suitable threshold value for the metabolic activity of each patient's tumor (adaptive threshold). MTV was calculated according to this value. We evaluted the relationship of mean values of MTV, SUVmax and SULmax with treatment response, local recurrence, distant metastasis and disease-related death. Receiver-operating characteristic (ROC) curve analysis was done to obtain optimal predictive cut-off values for MTV and SULmax which were found to have a predictive value. Local recurrence-free (LRFS), disease-free (DFS) and overall survival (OS) were examined according to these cut-offs. ResultsForty six patients had complete response, 15 had partial response, and 1 had stable disease 6weeks after the completion of treatment. Median follow-up of the entire cohort was 18months. Of 46 complete responders 10 had local recurrence, and of 16 partial or no responders 10 had local progression. Eighteen patients died. Adaptive threshold-based MTV had significant predictive value for treatment response (p=0.011), local recurrence/progression (p=0.050), and disease-related death (p=0.024). SULmax had a predictive value for local recurrence/progression (p=0.030). ROC curves analysis revealed a cut-off value of 14.00mL for MTV and 10.15 for SULmax. Three-year LRFS and DFS rates were significantly lower in patients with MTV≥14.00mL (p=0.026, p=0.018 respectively), and SULmax≥10.15 (p=0.017, p=0.022 respectively). SULmax did not have a significant predictive value for OS whereas MTV had (p=0.025). ConclusionAdaptive threshold-based MTV and SULmax could have a role in predicting local control and survival in head and neck cancer patients.

PALETTE: Final overall survival (OS) data and predictive factors for OS of EORTC 62072/GSK VEG110727, a randomized double-blind phase III trial of pazopanib versus placebo in advanced soft tissue sarcoma (STS) patients.

10009 Background: At ASCO 2011 we presented an increase in median progression-free survival (PFS) of pazopanib of 13 weeks (median 7 to 20 weeks; HR 0.31) in a randomized double-blind, placebo-controlled phase III trial in advanced non-adipocytic STS patients pretreated with chemotherapy. Now we present final OS data and predictive factors for OS, such as ethnicity, ancestry and well-known prognostic and predictive factors in STS patients treated with chemotherapy. Methods: At clinical cut-off median follow-up was 25 months. Comparison of both treatments for OS was performed using a two-sided log rank test stratified for number of prior lines of systemic therapy for advanced disease and WHO performance status (PS). HR was calculated with a 2-sided CI. A Kaplan-Meier OS curve was used. Baseline characteristics as potential predictive factors for OS were: PS (0 vs 1), number of lines of prior therapy for advanced disease (0-1 vs 2+), age (≤55 yrs vs &gt; 55 yrs), gender, ethnicity (Hispano or Latino vs other), geographic ancestry (White Caucasian, Asian, other), tumour grade (1-2 vs 3), histology (leiomyosarcoma vs synovial sarcoma vs other). A Cox model was fitted with the investigated factor, treatment arm and associated interaction factor. Data of all 369 randomized patients (123 placebo, 246 pazopanib) were used for the analyses. Results: Median OS (95% CI) was 10.7 (8.7-12.8) in the placebo versus 12.5 (10.6-14.8) months in the pazopanib group, HR: 0.86 (0.67-1.1). Post study protocol systemic treatment was administered in 63% of placebo, and in 49% of pazopanib treated patients. None of the investigated factors showed any statistical significant predictive value for OS. Conclusions: Although pazopanib demonstrated a statically significant increase in PFS compared to placebo, final OS analysis, which showed a trend in favor of pazopanib in patients with metastatic non-adipocytic STS, did not reach statistical significance. The factors studied were not predictive for OS.