An Epithelial-Mesenchymal Transition (EMT) Preoperative Nomogram for Prediction of Lymph Node Metastasis in Bladder Cancer (BLCA)

Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma

BACKGROUND

Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC).

METHODS

We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM.

RESULTS

A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients.

CONCLUSIONS

We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.

A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study

BACKGROUND

The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.

METHODS

Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).

RESULTS

A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI.

DISCUSSION

The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.

SMC1A facilitates gastric cancer cell proliferation, migration, and invasion via promoting SNAIL activated EMT

BACKGROUND

Structural maintenance of chromosomes protein 1 A (SMC1A) is a crucial subunit of the cohesion protein complex and plays a vital role in cell cycle regulation, genomic stability maintenance, chromosome dynamics. Recent studies demonstrated that SMC1A participates in tumorigenesis. This reseach aims to explore the role and the underlying mechanisms of SMC1A in gastric cancer (GC).

MATERIALS AND METHODS

RT-qPCR and western blot were used to examine the expression levels of SMC1A in GC tissues and cell lines. The role of SMC1A on GC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) were analyzed. Furthermore,the mechanism of SMC1A action was investigated.

RESULTS

SMC1A was highly expressed in GC tissues and cell lines. The high expression of SMC1A indicated the poor overall survival of GC patients from Kaplan-Meier Plotter. Enhancing the expression of SMC1A in AGS cells remarkably promoted cell proliferation in vitro and in vivo, migration and invasion, Conversely, knockdown of SMC1A in HGC27 cells inhibited cell proliferation, migration and invasion. Moreover, it's observed that SMC1A promoted EMT and malignant cell behaviors via regulating SNAIL.

CONCLUSION

Our study revealed that SMC1A promotes EMT process by upregulating SNAIL, which contributes to gastric cancer cell proliferation, migration and invasion. Therefore, targeting SMC1A may be a potential strategy to improve GC therapy.

Multidisciplinary Management and Radiotherapy Recommendations for Clinically and Pathologically Node-positive Bladder Cancer

There are limited data regarding the optimal management of patients with pelvic node-positive, but non-metastatic, bladder cancer. Increasing data demonstrate that this is a distinct clinical entity with outcomes bridging between bladder-confined muscle-invasive bladder cancer and metastatic advanced bladder cancer. Guidelines and staging systems have formalized the need to incorporate the unique considerations of management of pelvic node-positive bladder cancer. However, there remains an absence of a definite standard of care. Treatment options include systemic therapy alone, neoadjuvant chemotherapy followed by radical cystectomy, or bladder-preserving trimodality therapy. Furthermore, ongoing studies aim to determine the benefit of incorporating immunotherapy into these treatment paradigms. In this review article, we will discuss the key considerations for management of patients with pelvic node-positive bladder cancer.

A tumor microenvironment preoperative nomogram for prediction of lymph node metastasis in bladder cancer

Background

Growing evidence suggests that tumor metastasis necessitates multi-step microenvironmental regulation. Lymph node metastasis (LNM) influences both pre- and post-operative bladder cancer (BLCA) treatment strategies. Given that current LNM diagnosis methods are still insufficient, we intend to investigate the microenvironmental changes in BLCA with and without LNM and develop a prediction model to confirm LNM status.

Method

"Estimation of Stromal and Immune cells in Malignant Tumors using Expression data" (ESTIMATE) algorithm was used to characterize the tumor microenvironment pattern of TCGA-BLCA cohort, and dimension reduction, feature selection, and StrLNM signature construction were accomplished using least absolute shrinkage and selection operator (LASSO) regression. StrLNM signature was combined with the genomic mutation to establish an LNM nomogram by using multivariable logistic regression. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical utility. The testing set from the TCGA-BLCA cohort was used for internal validation. Moreover, three independent cohorts were used for external validation, and BLCA patients from our cohort were also used for further validation.

Results

The StrLNM signature, consisting of 22 selected features, could accurately predict LNM status in the TCGA-BLCA cohort and several independent cohorts. The nomogram performed well in discriminating LNM status, with the area under curve (AUC) of 75.1% and 65.4% in training and testing datasets from the TCGA-BLCA cohort. Furthermore, the StrLNM nomogram demonstrated good calibration with p >0.05 in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) revealed that the StrLNM nomogram had a high potential for clinical utility. Additionally, 14 of 22 stably expressed genes were identified by survival analysis and confirmed by qPCR in BLCA patient samples in our cohort.

Conclusion

In summary, we developed a nomogram that included an StrLNM signature and facilitated the preoperative prediction of LNM status in BLCA patients.