Lipopolysaccharide (LPS) from Helicobacter pylori (HP) plays an important role in gastric cancer occurrence and development. Toll-like receptor 4 (TLR4) and myeloid differential protein-2 (MD-2) are also reported to be involved in gastric cancer cell proliferation and invasion. CXC chemokine receptor 7 (CXCR7), a second receptor for CXCL12, has been detected in multiple types of tumor tissues. Nevertheless, the biological function and regulation of CXCR7 and its relationship with TLR4 and MD-2 in gastric cancer are not completely understood and therefore warrant further study. CXCR7 expression was examined in 150 gastric cancer tissues using immunohistochemistry (IHC). RT-PCR and western blotting were used to detect CXCR7 expression in several gastric cancer cell lines (SGC7901, AGS, MGC-803, MKN-45 and BGC823). shRNAs were designed using a pGPU6/GFP/Neo vector. A CCK-8 assay was used to assess cell proliferation, and transwell assays were performed to assess cell migration. In addition, a gastric cancer xenograft model was generated. The LPS-TLR4-MD-2 pathway elevates CXCR7 expression in SGC7901 cells, and TLR4/MD-2-mediated increases in CXCR7 levels modulate the proliferation and migration of tumor cells. Knockdown of TLR4 and MD-2 demonstrated that both are essential for LPS-induced CXCR7 expression, which in turn is responsible for LPS-induced SGC7901 cell proliferation and migration. Moreover, higher TLR4, MD-2 and CXCR7 expression was detected in gastric cancer tissues than in paracancerous normal control tissues. The expression levels of TLR4, MD-2 and CXCR7 were closely related to gastric cancer TNM stage and lymph node metastasis. In an animal model, significant differences in CXCR7 expression in tumor masses were observed between the control group and experimental group. The results of this study indicate that CXCR7 plays an important role in gastric cancer progression via inflammatory mechanisms, suggesting that CXCR7 could provide a basis for the development and clinical application of a targeted drug for gastric cancer.

KAI1 and epithelial-mesenchymal transition (EMT) is related to both angiogenesis and lymphangiogenesis and is an important target in new cancer treatment strategies. We aimed to investigate the KAI1 and marker of EMT expression and correlation with lymph node metastasis (LNM) and explore their prognostic impact in non-small cell lung cancer (NSCLC). Tumor tissue specimens from 312 resected patients with stage I–IIIA NSCLC were obtained. Immunohistochemistry was used to assess the expression of the molecular markers KAI1, E-cadherin (E-cad), vimentin, CD34, and D2-40. There were 153 N0 and 159 N+ patients. Tumor cell expression of KAI1and the marker of EMT, lymphatic vessel density (LVD), and microvessel density (MVD) were related to LNM. In multivariate analyses, the ages of patients, high tumor cell KAI1 expression, EMT, and the scores of MVD were independent factor of prognosis. Tumor cell KAI1 expression, EMT, LVD, and MVD correlate with LNM. Thus, the detection of KAI1, expression of markers of EMT, and the scores of MVD may be used as a potential indicator of NSCLC prognosis.

Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs). A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status. The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases. This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.