Lymph Node Metastasis In Cancer Research Articles

A liquid biopsy analysis of exosomal miRNA to detect lymph node metastasis in early gastric cancer.

469 Background: Additional surgical resection is required to achieve a complete cure in patients with early gastric cancer (EGC) due to the potential risk for lymph node metastasis (LNM) after pathological analysis. However, LNM is estimated to occur in approximately 10% of patients with high-risk EGC. In this study, we investigated a blood-based liquid biopsy assay of exosomal miRNA for the noninvasive detection of LNM in patients with high-risk EGC. Methods: Two genome-wide miRNA expression profiling datasets (GSE164174 and TCGA) were analyzed to prioritize biomarkers in pretreatment plasma samples from clinical training and validation cohorts of GC patients. An integrated exosomal miRNA panel was developed and a risk stratification model combining the miRNA panel with clinical risk factors was established. Results: Using comprehensive expression profiling of public datasets, we identified a transcriptomic panel of four miRNAs (miR-34b, miR-130a, miR-375, and miR-627) that robustly identified patients with LNM [area under the curve (AUC) = 0.86, 95% confidence interval (CI) = 0.77-0.92]. We assessed panel performance in a training cohort (AUC=0.86, 95% CI=0.67-0.96) and validated it in an independent validation cohort (AUC=0.84, 95% CI=0.64-0.95). Our risk stratification model was more accurate than the panel and was an independent predictor of LNM identification (AUC=0.97). Conclusions: A novel noninvasive, liquid biopsy-based method for patients with EGC may predict those traditionally classified as high-risk patients with LNM who are unlikely to benefit from surgical resection.

Detection of Central Compartment Lymph Node Metastasis of Thyroid Cancer: Usefulness of Intraoperative Thyroglobulin Measurement in Fine Needle Aspiration Washout with and Without Blue Dye Injection

Background/Objectives: The management of lymph node metastases of the central neck compartment (CNC) in differentiated thyroid carcinoma is debated. The intraoperative measurement of thyroglobulin (Tg) has gained attention in accurately detecting metastases, reducing unnecessary CNC dissections. Methods: A total of 37 patients underwent surgery. An intraoperative assay of thyroglobulin from fine needle aspiration (Tg-FNA) was performed on CNC lymph nodes, identified by blue dye injection in 15 patients (23 nodes, group A) and by palpation in 22 patients (35 nodes, group B). The Tg-FNA values were compared with histology to calculate the diagnostic accuracy. Results: In group A, the blue dye diffused widely, complicating lymph node identification: 2 were metastatic and 21 non-metastatic, with median Tg-FNA levels of 6236 ng/mL and 99.20 ng/mL, respectively. In group B, 8 were metastatic and 27 benign, with median Tg-FNA levels of 4063 ng/mL and 121 ng/mL (p < 0.0001), respectively. ROC analysis identified 500 ng/mL as a cutoff, achieving 100% sensitivity and 74% specificity in group B and 90% sensitivity and 70% specificity overall. Finally, among the non-metastatic lymph nodes, group A exhibited some cases of very high Tg-FNA values compared to group B, with lower accuracy for the cutoff, suggesting that colorant injection might lead to increased Tg-FNA levels. Conclusions: Blue dye injection showed low accuracy. Intraoperative Tg-FNA was reliable in detecting CNC metastases, although a higher cutoff is needed in this compartment compared to what has been reported for lateral lymph nodes. Lymphatic drainage and surgical manipulation might explain these findings. The careful interpretation of Tg-FNA in CNC should be adopted.

Retraction note: Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.

[This retracts the article on p. 85 in vol. 16, PMID: 38328326.].

Risk of Lymph Node Metastasis in T2 Colon Cancer: A Nationwide Population-Based Cohort Study.

Similar to T1 colon cancer (CC), risk stratification may guide T2 CC treatment and reduce unnecessary major surgery. In this study, prediction models were developed that could identify T2 CC patients with a lower risk of lymph node metastasis (LNM) for whom (intensive) follow-up after local treatment could be considered. A nationwide cohort study was performed involving pT2 CC patients who underwent surgery between 2012 and 2020, using data from the Dutch ColoRectal Audit, which were linked to the Nationwide Pathology Databank. Four machine learning models were evaluated to predict LNM. LNMs were found in 1877/9803 patients (19.1%). Independent risk factors included (younger) age (odds ratio [OR] 0.98, 95% confidence interval [CI] 0.979-0.990), left-sided CC (OR 1.5, 95% CI 1.4-1.7), poor differentiation (OR 1.7, 95% CI 1.4-2.2), and lymphovascular invasion (LVI; OR 4.1, 95% CI 3.6-4.7). A deficient mismatch repair (MMR) status significantly lowered the risk of LNM (OR 0.3, 95% CI 0.2-0.5). The general linear model demonstrated the highest prediction accuracy, achieving area under the receiver operating characteristic curves of 0.67 and 0.68, with good calibration. In the absence of risk factors, elderly patients (≥74years of age) had a predicted risk of LNM of 10.7%, yet up to 30% experienced postoperative complications, with mortality rates reaching up to 3.5%. Patients with a deficient MMR status had a predicted risk of LNM of 6.1% if LVI was absent and the tumor was well-differentiated. The risk of LNM should be weighed against surgical risks. The findings of this study will enable clinicians to make more deliberate considerations about these competing risks before making a shared decision.

Development of a prediction model based on Hemoglobin, Albumin, Lymphocyte count, and Platelet-score for lymph node metastasis in rectal cancer.

This study aimed to evaluate the ability of the preoperative Hemoglobin, Albumin, Lymphocyte count, and Platelet (HALP) score to predict lymph node metastasis (LNM) in patients with rectal cancer (RC) and improve prediction accuracy by incorporating clinical parameters. Data from 263 patients with RC were analyzed. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value (OCV) for the HALP score in predicting LNM. Based on this cutoff value, patients were divided into two groups. A baseline analysis was conducted to identify independent factors linked to LNM. A support vector machine (SVM) prediction model was developed, and its performance was evaluated using ROC, calibration curves, decision curve analysis, and Kolmogorov-Smirnov curve. The OCV for HALP score was 45.979. Patients were then classified into a low HALP group (n = 182) and a high HALP group (n = 81). The analysis found 21 clinical factors significantly associated with LNM. Among them, the key risk factors included high inflammatory status, poor nutritional condition, and a low HALP score. The SVM model incorporated these factors and showed robust predictive performance, with area under the curve values of 0.897, 0.813, and 0.750 for the training, validation, and testing datasets, respectively. The HALP score was significantly associated with LNM in RC patients. A machine learning model integrating the HALP score and inflammatory markers may be an effective tool for predicting LNM in RC.

Preoperative CT lymph node size as a predictor of nodal metastasis in resectable Colon cancer: a retrospective study of 694 patients

PurposeThis study aimed to investigate the efficacy of measuring lymph node size on preoperative CT imaging to predict pathological lymph node metastasis in patients with colon cancer to enhance diagnostic accuracy and improve treatment planning by establishing more reliable assessment methods for lymph node metastasis.MethodsWe retrospectively analyzed 1,056 patients who underwent colorectal resection at our institution between January 2004 and March 2020. From this cohort, 694 patients with resectable colon cancer were included in the study. We analyzed the relationship between lymph node size on preoperative CT imaging and lymph node metastasis identified on postoperative pathological examination.ResultsThe optimal cutoff values for the maximum long diameter and short diameter of regional lymph nodes on preoperative CT were identified as 6.5 mm and 5.5 mm, respectively, with an AUC of 0.7794 and 0.7755, respectively. Notably, the predictive accuracy varied by tumor location. Higher cutoff values were observed in the right-sided colon (maximum long diameter: 7.7 mm, maximum short diameter: 5.9 mm) compared to the left-sided colon (maximum long diameter: 5.8 mm, maximum short diameter: 5.2 mm).ConclusionLymph node size on preoperative CT is a significant predictor of pathological lymph node metastasis in colon cancer. Notably, the optimal cutoff values for predicting lymph node metastasis vary depending on the specific region within the colon.

Predictive Value of Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging and Diffusion-Weighted Imaging Findings for Sentinel Lymph Node Metastasis in Early-Stage Invasive Breast Cancer.

This retrospective study aimed to evaluate the predictive value of the preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) findings of mass lesions for predicting sentinel lymph node (SLN) metastasis in early breast cancer. A total of 310 patients with suspicious mass lesions detected in preoperative MRI who subsequently underwent surgery and SLN biopsy (SLNB) between September 2015 and September 2022 were analyzed. The relationship between DCE-MRI and DWI findings and SLNB positivity was analyzed. SLNB was positive for SLN metastasis in 108 of 310 lesions. Younger age (p = 0.001) and larger lesion size (p < 0.001) were found to be associated with SLNB positivity. Findings associated with SLN metastasis included peritumoral edema in 53%, adjacent vessel sign (AVS) in 81%, and increased whole-breast vascularity (WBV) in 58% of patients with positive SLNB (p < 0.001). The SLNB positivity rate was higher in mass lesions with DCE-MRI findings of heterogenous enhancement pattern (p = 0.003), medium or rapid initial phase enhancement (p = 0.001), and washout delayed phase kinetic curve (p = 0.001). It was found that lower tumoral apparent diffusion coefficient (ADC) values (p = 0.003) and higher peritumoral/tumoral ADC ratios (p = 0.018) increased the probability of encountering SLN metastasis. Patient age, presence of peritumoral edema, presence of AVS, increased WBV, and initial phase kinetic curve of the lesions on MRI were found to be associated with SLN metastasis. We found that younger age and MR findings obtained from the perilesional area of breast cancer may be helpful in the preoperative prediction of SLN metastasis.

Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019–June 2023). The validation set included separate data points (n = 178, January 2019–June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability.

Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features.

Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize to lateral nodes, often undetected by standard preoperative ultrasonography. Although multiple models exist to identify SLNM, they are inadequate for clinically node-negative (cN0) patients, resulting in underestimated metastatic risks and compromised treatment effectiveness. Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies. Our study conducted a retrospective analysis of 485 newly diagnosed primary PTC patients, divided into training and external validation cohorts. Patients were categorized into SLNM and non-SLNM groups based on follow-up outcomes and postoperative pathology. We collected preoperative clinicopathological data and extracted, standardized radiomics features from elastography imaging to develop various ML models. These models were internally validated using radiomics and clinicopathological data, with the optimal model's feature importance analyzed through the Shapley Additive Explanations (SHAP) approach and subsequently externally validated. In our study of 485 patients, 67 (13.8%) exhibited SLNM. The extreme gradient boosting (XGBoost) model, integrating elastography radiomics with clinicopathological data, demonstrated superior performance in both internal and external validations. SHAP analysis identified five key determinants of SLNM: three radiomics features from elastography images, one clinical variable, and one pathological variable. Our evaluation highlights the XGBoost model, which integrates elastography radiomics and clinicopathological data, as the most effective ML approach for the prediction of SLNM in cN0 PTC patients with increased risk of LNM. This innovative model significantly enhances the accuracy of risk assessments for SLNM, enabling personalized treatments that could reduce postoperative metastases in these patients.

A combined diagnostic model including middle rectal artery visualization for predicting lateral lymph node metastasis in rectal cancer.

This study attempted to establish a combined diagnostic model encompassing visualization of the middle rectal artery (MRA) and other imaging features to improve the diagnostic efficiency of lateral lymph node (LLN) metastasis, which is crucial for clinical decision-making in rectal cancer. One hundred eleven patients receiving bilateral or unilateral lymph node dissection were enrolled, and 140 cases of LLN status on a certain unilateral pelvic sidewall were selected. Enhanced computed tomography (CT) was used to determine whether MRA was visible. Multivariable regression was used to establish a diagnostic model combining MRA visualization with other imaging features to predict LLN metastasis. Receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to test the diagnostic efficacy for LLN metastasis. Ten-fold cross-validation was completed to internally validate the diagnostic model. Of the 140 LLNs harvested from 111 patients, 76 were positive and 64 were negative for metastases, respectively. The diagnostic model combining the MRA visualization and lymph node short diameter showed a greater efficiency than a single scale (AUC = 0.945, 95% confidence interval = 0.893-0.976, P < 0.001). The mean cross-validated AUC was 0.869 (95% confidence interval = 0.835-0.903). Our results establish a combined diagnostic model with the help of MRA visualization to yield a high diagnostic efficiency of LLN metastasis in rectal cancer.

Single-cell RNA-sequencing and spatial transcriptomic analysis reveal a distinct population of APOE- cells yielding pathological lymph node metastasis in papillary thyroid cancer.

Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood. Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression. We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis. We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC. A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

The MIR181A2HG/miR-5680/VCAN-CD44 Axis Regulates Gastric Cancer Lymph Node Metastasis by Promoting M2 Macrophage Polarization.

Lymphatic metastasis in gastric cancer (GC) profoundly influences its prognosis, but the precise mechanism remains elusive. In this study, we identified the long noncoding RNA MIR181A2HG as being upregulated in GC and associated with LNs metastasis and prognosis. The expression of MIR181A2HG in GC was identified through bioinformatics screening analysis and qRT-PCR validation. Both invitro and invivo functional studies revealed that MIR181A2HG facilitates lymphangiogenesis and lymphatic metastasis. Techniques such as immunofluorescence, immunohistochemistry, qRT-PCR, ELISA, CHIP, RNA-pulldown, luciferase reporter assay, and Co-IP were employed to investigate the mechanism of MIR181A2HG in LNs metastasis of GC. MIR181A2HG overexpressed in GC signifies an unfavorable prognosis and drives M2 polarization of TAMs enhancing lymphangiogenesis. Mechanistically, MIR181A2HG/miR-5680 axis as a novel ceRNA regulatory axis to upregulate versican (VCAN). On one hand, VCAN interacts with CD44 receptors on the surface of TAMs through paracrine secretion, promoting M2 macrophage polarization and subsequently enhancing the secretion of VEGF-C, ultimately facilitating lymphangiogenesis. On the other hand, VCAN binds to CD44 receptors on the surface of GC cells through autocrine secretion, activating the Hippo pathway and upregulating SP1, thereby promoting the transcription of MIR181A2HG and establishing a feedback loop driving lymphatic metastasis. This study highlights the pivotal role of MIR181A2HG in GC progression and LNs metastasis. MIR181A2HG-based targeted therapy would represent a novel strategy for GC.

Development andValidation of a Nomogram Based on Multiparametric MRI for Predicting Lymph Node Metastasis in Endometrial Cancer: ARetrospective Cohort Study.

To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC). We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86). Based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) arterial phase and equilibrium phase images, radiomics features were extracted. Clinical characteristics were determined using multivariate logistic regression analysis. Subsequently, eight machine learning classification algorithms were employed to construct the radiomics model and clinical models, from which the best algorithm was selected. Ultimately, the radiomics and clinical features were combined to establish the radiomics nomogram. The efficacy of each model was appraised through receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). The LR algorithm demonstrated superior predictive accuracy, with areas under the curve (AUCs) of 0.903 and 0.824 in the test and validation sets, respectively. Radiomics nomograms showed better predictive performance compared to clinical models or radiomics models, the AUCs in the test and external validation set were 0.900 (95% confidence interval [CI]: 0.784-1.000) and 0.858 (95%CI: 0.750-0.966), respectively. The calibration curve and DCA indicated that the nomogram had excellent predictive performance. The nomogram based on radiomics features and clinical parameters could effectively predict LNM in patients with EC, thus providing a basis for clinicians to develop individualized treatment plans preoperatively.

Risk factors for lymph node metastasis and invasion depth in early gastric cancer: Analysis of 210 cases.

Gastric cancer is the leading cause of cancer-related deaths worldwide. Early gastric cancer (EGC) is often associated with the risk of lymph node metastasis, which influences treatment decisions. Despite the use of enhanced computed tomography, the prediction of lymph node involvement remains challenging. To investigate the risk factors for lymph node metastasis and invasion depth in patients with EGC. In total, 210 patients with pathologically diagnosed EGC were included in this study. Univariate and multivariate statistical analyses were used to predict risk factors for lymph node metastasis and invasion depth in patients with EGC. Among the 210 patients, 27 (12.9%) had lymph node metastases. Of the 117 patients with submucosal gastric cancer, 24 (20.5%) had lymph node metastases. Both univariate and multivariate analyses indicated that the depth of invasion in EGC was a risk factor for lymph node metastasis in these patients. Additionally, pathological type was identified as a risk factor for cancer cell invasion in patients with EGC. EGC invasion depth, not tumor type, size, age, sex, or location, predicts lymph node spread. Tumor type, not size, age, sex, or location, predicts cancer cell invasion.

Differential Gene Expression Analysis of The Cancer Genome Atlas Messenger Ribonucleic Acid Sequencing Data from Male Patients with and without Lymph Node Metastasis in Tongue Cancer

Gene expression plays a crucial role in progression, invasion, and metastasis in many groups of oral squamous cell carcinoma. Based on extensive patient data, no research has been done so far on the differences in gene expression between groups of male patients with tongue squamous cell carcinoma who drink alcohol and those who do not, about lymph node metastasis. Our study analysed differential gene expression in male patients with and without lymph node metastasis in tongue cancer, utilising messenger ribonucleic acid (mRNA) sequencing data from The Cancer Genome Atlas (TCGA) program. Analysis was performed using R and bioinformatics tools to process mRNA sequencing data, compare differential gene expression, and examine functional signaling pathways and survival analysis. After screening the database, 38 out of 74 cases with metastasis were selected for the analysis. The computational analysis identified a significant increase in cancer invasion transcription factors, such as GATA2, SP1 and MYC in the metastatic group, whereas CREB1 was more associated with the nonmetastatic group. Functional analysis suggested new prognostic biomarkers for unfavourable outcomes (TRIML2 and EXOSC4) and favourable outcomes (HS3ST4 and SFTPA1). New suggested markers for oral and tongue cancer such as TRIML2, EXOSC4 are known for their role in non-oral cancer including lung and liver cancer. Especially head and neck squamous cell carcinoma (HNSCC) survival analysis and protein atlas investigation confirmed the indirect biological roles of found markers. The study indicates that TRIML2 and EXOSC4 could serve as biological markers for predicting lymph node metastasis in tongue squamous cell carcinoma among male patients who consume alcohol. These findings suggest that gene expression profiles may play a role in the progression and metastasis of tongue cancer. Further research is needed to validate these results and to develop targeted therapies for patients with tongue cancer.

Diagnostic performance of depth of invasion, thickness, and styloglossus and hyoglossus muscle invasion on magnetic resonance imaging in predicting potential neck lymph node metastasis in clinical N0 tongue cancer.

To evaluate previously reported quantitative (tumor thickness 11mm and depth of invasion [DOI] 7.5mm) and qualitative (styloglossus/hyoglossus muscle invasion [SHMI]) magnetic resonance imaging (MRI) parameters for predicting occult neck node metastasis in clinical N0 oral tongue squamous cell carcinoma. This single-center retrospective study included 76 patients. MRI images were independently reviewed by two radiologists for tumor thickness, DOI, and SHMI. Statistical analysis assessed the predictive capability of these parameters for 2-year potential lymph node metastasis. Among the 76 cases, 30.2% developed 2-year potential lymph node metastasis. For tumor thickness ≥ 11mm, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 0.46, 0.68, 0.37, 0.75, and 0.61, respectively. DOI ≥ 7.5mm exhibited a sensitivity, specificity, PPV, NPV, and accuracy of 0.73, 0.59, 0.42, 0.84, and 0.63, respectively. SHMI demonstrated a sensitivity, specificity, PPV, NPV, and accuracy of 0.87, 0.51, 0.46, 0.89, and 0.63, respectively. DOI ≥ 7.5mm and SHMI demonstrated comparable diagnostic accuracy in predicting neck metastasis, surpassing tumor thickness of > 11mm. These findings underscore their potential utility in guiding decisions concerning elective neck dissection.

LRP1B mutation is associated with lymph node metastasis in endometrial carcinoma: A clinical next-generation sequencing study.

This study aims to investigate the mutation status and protein expression of low-density lipoprotein receptor-related protein 1B (LRP1B) in endometrial cancer, and analyze its association with lymph node metastasis (LNM) in endometrial cancer. Targeted next-generation sequencing (NGS) was conducted on both tumor tissues and paired blood DNA obtained from 94 endometrial cancer patients, followed by comprehensive analysis. Additionally, immunohistochemistry (IHC) was used to explore the correlation between LRP1B protein expression levels, its gene mutation status, and LNM. LRP1B mutation was observed in 19 patients (20.2%). Our results revealed that LRP1B mutation frequencies were significantly different between endometrial cancer with or without LNM (P = 0.038). Multivariate analysis indicated that LRP1B mutation was a favorable predictor (odds ratio 0.09; 95% confidence interval 0.01-0.95; P = 0.045) for LNM in endometrial cancer. Further analysis revealed that combination of LRP1B mutation with clinical variants (LVSI and histological subtype) yielded a higher area under the curve value of 0.871) and patients harboring LRP1B mutated-type were less likely to develop LNM. On integrated analysis, the concordance between LRP1B NGS and LRP1B IHC was 73.3%. This study utilizes targeted NGS to uncover the relationship between LRP1B mutation and LNM status, contributing to the development of primary prevention and proactive treatment strategies.

FNDC1 Facilitates Proliferation, Migration, and Invasion of Breast Cancer Cells Through Modulating Wnt/β-Catenin Pathway.

Breast cancer is the most common malignant cancer and the leading fatal cancer in women around the world. Fibronectin type III domain-containing protein 1 (FNDC1) has been demonstrated to play crucial roles in various tumors. However, the function of FNDC1 in breast cancer remains to be addressed. Increased FNDC1 expression was found in breast cancer that is associated with individual cancer stages and lymph node metastasis through UALCAN analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot assays indicated that FNDC1 expression was up-regulated in breast cancer cells. The results of Cell Counting Kit-8 and colony formation assays indicated that FNDC1 promoted the proliferation of breast cancer cells. Moreover, FNDC1 knockdown suppressed xenograft tumor growth and inhibited the levels of FNDC1 and marker of proliferation Ki-67. Transwell assay demonstrated that FNDC1 promoted the migration and invasion of breast cancer cells. Importantly, mechanism analysis implied that FNDC1 promoted the Wnt/β-catenin signaling pathway. Notably, Wnt/β-catenin activation with LiCl significantly enhanced the proliferation and epithelial-mesenchymal transformation (EMT) inhibition effect of silencing FNDC1, whereas Wnt/β-catenin inhibition with XAV-939 significantly weakened the proliferation and EMT promotion effect of FNDC1. Analysis of β-catenin expression in the nucleus and cytoplasm showed that FNDC1 promoted β-catenin nuclear translocation. These data suggested that FNDC1 exerts its oncogene function through modulating Wnt/β-catenin signaling pathway. In conclusion, FNDC1 promotes cell proliferation, migration, invasion, and EMT through modulating Wnt/β-catenin signaling pathway in breast cancer, providing a new idea for the development of breast cancer therapeutic targets.

Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and poses a serious threat to human health. Due to the advances in lung cancer screening, more and more clinical T1 NSCLC defined as a tumor with a maximum diameter of 3cm surrounded by lung tissue or visceral pleura have been detected and have achieved favorable treatment outcomes, greatly improving the prognosis of NSCLC patients. However, the preoperative lymph node staging and intraoperative lymph node dissection patterns of operable clinical T1 NSCLC are still subject to much disagreement, as well as the heterogeneity between primary tumors and metastatic lymph nodes poses a challenge in designing effective treatment strategies. This article comprehensively describes the clinical risk factors of clinical T1 NSCLC lymph node metastasis, and its invasive and non-invasive prediction, focusing on the genetic heterogeneity between the primary tumor and the metastatic lymph nodes, which is significant for a thoroughly understanding of the biological behavior of early-stage NSCLC.

Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer.

Extended pelvic lymph node dissection is a crucial surgical technique for managing intermediate to high-risk prostate cancer. Accurately predicting lymph node metastasis before surgery can minimize unnecessary lymph node dissections and their associated complications. This study assessed the efficacy of various machine learning models for predicting lymph node metastasis in a cohort of Japanese patients who underwent robot-assisted laparoscopic radical prostatectomy. Data from 625 patients who underwent extended pelvic lymph node dissection or standard dissection with lymph node metastasis between October 2010 and February 2023 were analyzed. Four machine learning models-Random Forest, Light Gradient-Boosting Machine, Logistic Regression, and Support Vector Machine-were used to predict lymph node metastasis. Their performance was assessed using receiver operating characteristic curves, a decision curve analysis, and predictive values at different thresholds. Lymph node metastasis was observed in 34 patients (5.4%). The Light Gradient-Boosting Machine had the highest AUC of 0.924, followed by the Random Forest model with an AUC of 0.894. The decision curve analysis indicated substantial net benefits for both models, particularly at low threshold probabilities. The Light Gradient-Boosting Machine demonstrated superior accuracy, achieving 95.6% at the 0.05 threshold and 96.7% at the 0.10 threshold, outperforming other models and conventional nomograms in the validation dataset. Machine learning models, especially Light Gradient-Boosting Machine and Random Forest, show significant potential for predicting lymph node metastasis in prostate cancer, thereby aiding in reducing unnecessary surgical interventions.