Impairment of natural killer (NK) cell activity is an important mechanism of tumor immunoevasion. T cell immunoglobulin domain and mucin domain-3 (Tim-3) is an activation-induced inhibitory molecule, inducing effector lymphocyte exhaustion in chronic viral infection and cancers. However, its function in NK cells in human esophageal cancer remains unclear. We prospectively collected peripheral blood and tumor samples from 53 patients with esophageal cancer. Peripheral and tumor-infiltrating NK cells were analyzed for Tim-3, Annexin V, CD69, CD107a and IFN-γ expression by flow cytometry. Quantitative real-time PCR was used to test relative mRNA expression of IFN-γ, granzyme B, perforin and NKG2D in sorted Tim-3+ NK cells and Tim-3− NK cells, respectively. NK cells isolated from healthy donors were treated with recombinant TNF-α to induce Tim-3 expression. Tim-3 and TNF-α mRNA levels in tumor tissues were measured in both humans and mice. Finally, associations between NK cell frequencies with pathological parameters were investigated. We observed up-regulation of Tim-3 expression on NK cells from esophageal cancer patients, especially at the tumor site. Furthermore, tumor-infiltrating NK cells with high Tim-3 expression exhibited a phenotype with enhanced dysfunction. In vitro, Tim-3 expression on NK cells isolated from blood of healthy donors can be induced by recombinant TNF-α via NF-κB pathway. In both animal models and patients, the Tim-3 level was positively correlated with TNF-α expression in esophageal cancer tissues. Finally, higher Tim-3 level on tumor-infiltrating NK cells is correlated with tumor invasion, nodal status and poor stage in patients with esophageal cancer. Taken together, Tim-3 may play a crucial role to induce NK cell dysfunction in tumor microenvironment and could serve as a potential biomarker for prognosis of esophageal cancer.

Objectives Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers. Methods This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps. Results The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively. Conclusions By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

Lymph node metastasis is a major factor that affects prognosis in patients with lung adenocarcinoma (LUAD). In some cases, lymph node metastasis has already occurred when the primary tumors are still small (i.e., early T stages), however, relevant studies on early lymph node metastasis are limited, and effective biomarkers remain lacking. This study aimed to explore new molecular biomarker for early lymph node metastasis in LUAD using transcriptome sequencing and experimental validation. Here, we performed transcriptome sequencing on tissues from 16 matched patients with Stage-T1 LUAD (eight cases of lymph node metastasis and eight cases of non-metastasis), and verified the transcriptome profiles in TCGA, GSE68465, and GSE43580 cohorts. With the bioinformatics analysis, we identified a higher abundance of M0 macrophages in the metastatic group using the CIBERSORT algorithm and immunohistochemistry (IHC) analysis and the enrichment of the epithelial–mesenchymal transition (EMT) pathway was identified in patients with higher M0 infiltration levels. Subsequently, the EMT hallmark gene SPP1, encoding secreted phosphoprotein 1 (SPP1), was identified to be significantly correlated with macrophage infiltration and M2 polarization, and was determined to be a key risk indicator for early lymph node metastasis. Notably, SPP1 in the blood, as detected by enzyme-linked immunosorbent assay (ELISA) showed a superior predictive capability for early lymph node metastasis [area under the curve (AUC) = 0.74]. Furthermore, a long non-coding RNA (lncRNA, AC037441), negatively correlated with SPP1 and macrophage infiltration, had also been identified and validated to be involved in the regulation of early lymph node metastasis. In conclusion, we revealed the potential role of macrophages in lymph node metastasis and identified the macrophage-related gene SPP1 as a potential biomarker for early lymph node metastasis in LUAD.

Background: Plasma cell-free DNA (cfDNA) methylation has shown promising results in the early detection of multiple cancers recently. Here, we conducted a study to investigate the performance of cfDNA methylation in the early detection of esophageal cancer (ESCA). Methods: Specific methylation markers for ESCA were identified and optimized based on esophageal tumor and paired adjacent tissues (n = 24). Age-matched participants with ESCA (n = 85), benign esophageal diseases (n = 10), and healthy controls (n = 125) were randomized into the training and test sets to develop a classifier to differentiate ESCA from healthy controls and benign esophageal disease. The classifier was further validated in an independent plasma cohort of ESCA patients (n = 83) and healthy controls (n = 98). Results: In total, 921 differentially methylated regions (DMRs) between tumor and adjacent tissues were identified. The early detection classifier based on those DMRs was first developed and tested in plasma samples, discriminating ESCA patients from benign and healthy controls with a sensitivity of 76.2% (60.5–87.9%) and a specificity of 94.1% (85.7–98.4%) in the test set. The performance of the classifier was consistent irrespective of sex, age, and pathological diagnosis (P > 0.05). In the independent plasma validation cohort, similar performance was observed with a sensitivity of 74.7% (64.0–83.6%) and a specificity of 95.9% (89.9–98.9%). Sensitivity for stage 0–II was 58.8% (44.2–72.4%). Conclusion: We demonstrated that the cfDNA methylation patterns could distinguish ESCAs from healthy individuals and benign esophageal diseases with promising sensitivity and specificity. Further prospective evaluation of the classifier in the early detection of ESCAs in high-risk individuals is warranted.

A pan-cancer analysis of the oncogenic role of secreted phosphoprotein 1 (SPP1) in human cancers Yafei Liu, Guanchao Ye, Bo Dong, Lan Huang, Chunyang Zhang, Yinliang Sheng, Bin Wu, Lu Han, Chunli Wu, Yu Qi