Background: Lung cancer is the leading cause of cancer-related death worldwide. Although the macrophages can affect the development of tumor, the contribution of macrophages to the prognosis of non-small-cell lung cancer (NSCLC) is still controversial. Moreover, anti-PD-1 therapy can redirect macrophages from an M2 to an M1 phenotype, suggesting that tumor PD-L1 may affect the prognostic role of macrophages. Therefore, in this study, we aimed to display a macrophage landscape to clarify the function of macrophages, considering the localization and polarization of the macrophages, and evaluate the effect of M2 macrophages and tumor PD-L1 in combination on the prognosis of NSCLC. Methods: We performed multiplex quantitative immunofluorescence staining of pan-cytokeratin (CK), CD68, CD163, PD-L1, and DAPI on one tissue specimen simultaneously from 137 NSCLC patients. Results: M2 macrophages, involved marginM2 (M2 macrophages in tumor stroma), and centralM2 (M2 macrophages infiltrating into tumor islets) increased as the tumor stage increased. More macrophages were found in lung squamous cell carcinoma (LUSC) patients, patients with wild-type EGFR, and smokers than in patients with lung adenocarcinoma (LUAD), patients with EGFR mutations, and non-smokers. Infiltration of centralM2 was an independent prognostic factor of poor overall survival (OS) and disease-free survival (DFS) for NSCLC patients (P<0.05), which was superior to total macrophages and total M2 macrophages. Moreover, patients with centralM2^less,PD-L1^−, tumors showed the best OS and DFS, while the patients with centralM2^more,PD-L1^+, tumors showed the worst OS and DFS, and the two groups with centralM2^less,PD-L1^+, and centralM2^more,PD-L1^−, were in the middle (P=0.002, 0.034, respectively). Conclusion: Tumor islet-infiltrating M2 macrophages influence the prognosis of NSCLC patients. The analysis of M2 macrophages and tumor PD-L1 in combination may enhance the accuracy of prognostic prediction. This study provides a new understanding of macrophages in the development of NSCLC through the analysis of macrophage landscape.

Programmed death-ligand 1 (PD-L1) is an immunosuppressor that plays an important role in cancer treatments. Although majority of the studies demonstrated that PD-L1 expression was regulated by cellular intrinsic and extrinsic controls, and IFN-γ was a key molecule of extrinsic control, other studies imply that other cytokines play important roles in PD-L1 expression. In this study, we investigated the regulation of PD-L1 by chemokine signaling pathway in gastric cancer (GC) cells. Bioinformatics was used to explore the PD-L1-related genes in GC and propose a hypothesis. PD-L1 and CXCR3 expression were detected by western blot in SGC7901 and MKN74 cell lines. Meanwhile, PD-L1 and CXCR3 expressions were immunohistochemically assessed for their relevance. Moreover, PD-L1, pSTAT3 and pAkt were detected after treatment with CXCL9/10/11. Furthermore,PD-L1, pSTAT3 and pAkt were evaluated after blocking chemokine signaling in SGC7901 cells. Based on online database analysis, CXCL9/10/11-CXCR3 is proposed to upregulate PD-L1 expression by activating the STAT and PI3K-Akt pathways. This hypothesis was confirmed by in vitro and vivo experiments. CXCR3 and PD-L1 were expressed in GC cell lines and tissues, and the expression of CXCR3 and PD-L1 was positively related. PD-L1 was upregulated after treatment with CXCL9/10/11, accompanied by activation of STAT3 and Akt. After blocking chemokine signaling, upregulation of PD-L1 and activation of STAT3 and Akt were diminished. CXCL9/10/11-CXCR3 upregulated the expression of PD-L1 by activating the STAT and PI3K-Akt signaling pathways in GC cells. There was a significant positive correlation between the expression of PD-L1 and CXCR3 in gastric cancer patient tissues.

Epithelial-mesenchymal transition (EMT) and ferroptosis are two important processes in biology. In tumor cells, they are intimately linked. We used single-cell RNA sequencing to investigate the regulatory connection between EMT and ferroptosis tendency in LUAD epithelial cells. We used Seurat to construct the expression matrix using the GEO dataset GSE131907 and extract epithelial cells. We found a positive correlation between the trends of EMT and ferroptosis tendency. Then we used SCENIC to analyze differentially activated transcription factors and constructed a molecular regulatory directed network by causal inference. Some ferroptosis markers (GPX4, SCP2, CAV1) were found to have strong regulatory effects on EMT. Cell communication networks were constructed by iTALK and implied that Ferro_High_EMT_High cells have a higher expression of SDC1, SDC4, and activation of LGALS9-HARVCR2 pathways. By deconvolution of bulk sequencing, the results of CIBERSORTx showed that the co-occurrence of ferroptosis tendency and EMT may lead to tumor metastasis and non-response to immunotherapy. Our findings showed there is a strong correlation between ferroptosis tendency and EMT. Ferroptosis may have a promotive effect on EMT. High propensities of ferroptosis and EMT may lead to poor prognosis and non-response to immunotherapy.

Background/Aims: The transcription cofactor limb-bud and heart (LBH) is involved in embryonic development. However, its role in human lung cancer, especially lung adenocarcinoma (LUAD), remains unclear. Methods: A public database and tissue microarray (TMA) were used to compare differences in LBH expression and its relationship with clinical characteristics. Tissue from an additional 70 LUAD patients with follow-up records was used to explore the correlation of LBH expression with prognosis. Cellular and molecular studies validated the role of LBH in LUAD growth and invasion. Results: LBH was significantly down-regulated in lung cancer tissue samples and was correlated with the prognosis and clinical characteristics of lung cancer patients based on a public database and TMA. Survival analysis revealed that LBH-negative expression was associated with poor overall survival of LUAD patients (P = 0.021). Cox regression analysis showed that LBH expression status was a favorable independent prognostic factor (hazard ratio = 0.120, 95% confidence interval = 0.016–0.894, P = 0.039). LBH knockdown accelerated LUAD cell proliferation, migration, and invasion. Furthermore, bioinformatics analysis indicated that LBH was significantly related to the cell adhesion pathway. Western blot analysis confirmed that LBH could regulate the expression of integrin family members (integrin-α1, integrin-α2, integrin-α4, integrin-αv, and integrin-β4). Conclusion: Our data suggest that LBH plays an important role in lung cancer. Importantly, LBH is an independent prognostic factor in LUAD and can attenuate cell growth and invasion. LBH may be a potential prognostic biomarker in LUAD patients.

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.