OBJECTIVE: Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.
METHODS: Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.
RESULTS: In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values.
CONCLUSIONS: The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.

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To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.

OBJECTIVE: To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer.
METHODS: A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis.
RESULTS: Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI.
CONCLUSIONS: The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery.
CONCLUSIONS: Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI\'s potential in the preoperative assessment of rectal cancer.
CONCLUSIONS: Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

OBJECTIVE: Dysfunctional lymphangiogenesis is pivotal for various pathological processes including tumor lymph node metastasis which is a crucial cause of therapeutic failure for ESCC. In this study, we aim to elucidate the molecular mechanisms and clinical relevance of Zinc-finger protein ZNF468 in lymphangiogenesis and lymphatic metastasis in ESCC.
METHODS: Immunohistochemistry, Western blot, Kaplan-Meier plotter analysis and Gene Set Enrichment Analysis were preformed to detect the association of ZNF468 with lymphangiogenesis and poor prognosis in ESCC patients. Foot-pads lymph node metastasis model, tube formation assay, 3D-culture assay and invasion assay were preformed to verify the effect of ZNF468 on lymphangiogenesis and lymph node metastasis. CUT&Tag analysis, immunoprecipitation and mass spectrometry analysis and ChIP-PCR assay were preformed to study the molecular mechanisms of ZNF468 in lymphangiogenesis.
RESULTS: We found that ectopic expression of ZNF468 was correlated with higher microlymphatic vessel density in ESCC tissues, leading to poorer prognosis of ESCC patients. ZNF468 enhanced the capacity of lymphangiogenesis and promoted lymphatic metastasis in ESCC both in vitro and in vivo. However, silencing ZNF468 reversed these phenotypes in ESCC. Mechanically, we demonstrated that ZNF468 recruits the histone modification factors (PRMT1/HAT1) to increase the levels of H4R2me2a and H3K9ac, which then leads to the recruitment of the transcription initiation complex on the VEGF-C promoter, ultimately promoting the upregulation of VEGF-C transcription. Strikingly, the promoting effect of lymphatic metastasis induced by ZNF468 in ESCC was abrogated by targeting PRMT1 using Arginine methyltransferase inhibitor-1 or silencing VEGF-C. Furthermore, we found that the activation of PI3K/AKT and ERK1/2 signaling is required for ZNF468-medicated lymphatic metastasis in ESCC. Importantly, the clinical relevance between ZNF468 and VEGF-C were confirmed not only in ESCC samples and but also in multiple cancer types.
CONCLUSIONS: Our results identified a precise mechanism underlying ZNF468-induced epigenetic upregulation of VEGF-C in facilitating lymphangiogenesis and lymph node metastasis of ESCC, which might provide a novel prognostic biomarker and potential therapeutic for ESCC patients.

BACKGROUND: Oral cancer poses a significant health challenge due to limited treatment protocols and therapeutic targets. We aimed to investigate the invasive margins of gingivo-buccal oral squamous cell carcinoma (GB-OSCC) tumors in terms of the localization of genes and cell types within the margins at various distances that could lead to nodal metastasis.
METHODS: We collected tumor tissues from 23 resected GB-OSCC samples for gene expression profiling using digital spatial transcriptomics. We monitored differential gene expression at varying distances between the tumor and its microenvironvent (TME), and performed a deconvulation study and immunohistochemistry to identify the cells and genes regulating the TME.
RESULTS: We found that the tumor-stromal interface (a distance up to 200 µm between tumor and immune cells) is the most active region for disease progression in GB-OSCC. The most differentially expressed apex genes, such as FN1 and COL5A1, were located at the stromal ends of the margins, and together with enrichment of the extracellular matrix (ECM) and an immune-suppressed microenvironment, were associated with lymph node metastasis. Intermediate fibroblasts, myocytes, and neutrophils were enriched at the tumor ends, while cancer-associated fibroblasts (CAFs) were enriched at the stromal ends. The intermediate fibroblasts transformed into CAFs and relocated to the adjacent stromal ends where they participated in FN1-mediated ECM modulation.
CONCLUSIONS: We have generated a functional organization of the tumor-stromal interface in GB-OSCC and identified spatially located genes that contribute to nodal metastasis and disease progression. Our dataset might now be mined to discover suitable molecular targets in oral cancer.

BACKGROUND: To identify the cut-off values for the number of metastatic lymph nodes (nMLN) and lymph node ratio (LNR) that can predict outcomes in patients with FIGO 2018 IIICp cervical cancer (CC).
METHODS: Patients with CC who underwent radical hysterectomy with pelvic lymphadenectomy were identified for a propensity score-matched (PSM) cohort study. A receiver operating characteristic (ROC) curve analysis was performed to determine the critical nMLN and LNR values. Five-year overall survival (OS) and disease-free survival (DFS) rates were compared using Kaplan-Meier and Cox proportional hazard regression analyses.
RESULTS: This study included 3,135 CC patients with stage FIGO 2018 IIICp from 47 Chinese hospitals between 2004 and 2018. Based on ROC curve analysis, the cut-off values for nMLN and LNR were 3.5 and 0.11, respectively. The final cohort consisted of nMLN ≤ 3 (n = 2,378) and nMLN > 3 (n = 757) groups and LNR ≤ 0.11 (n = 1,748) and LNR > 0.11 (n = 1,387) groups. Significant differences were found in survival between the nMLN ≤ 3 vs the nMLN > 3 (post-PSM, OS: 76.8% vs 67.9%, P = 0.003; hazard ratio [HR]: 1.411, 95% confidence interval [CI]: 1.108-1.798, P = 0.005; DFS: 65.5% vs 55.3%, P < 0.001; HR: 1.428, 95% CI: 1.175-1.735, P < 0.001), and the LNR ≤ 0.11 and LNR > 0.11 (post-PSM, OS: 82.5% vs 76.9%, P = 0.010; HR: 1.407, 95% CI: 1.103-1.794, P = 0.006; DFS: 72.8% vs 65.1%, P = 0.002; HR: 1.347, 95% CI: 1.110-1.633, P = 0.002) groups.
CONCLUSIONS: This study found that nMLN > 3 and LNR > 0.11 were associated with poor prognosis in CC patients.