Lung cancer is a kind of malignant tumor with rapid progression and poor prognosis. Distant metastasis has been the main cause of mortality among lung cancer patients. Bone is one of the most common sites. Among all lung cancer patients with bone metastasis, most of them are osteolytic metastasis. Some serious clinical consequences like bone pain, pathological fractures, spinal instability, spinal cord compression and hypercalcemia occur as well. Since the severity of bone metastasis in lung cancer, it is undoubtedly necessary to know how lung cancer spread to bone, how can we diagnose it and how can we treat it. Here, we reviewed the process, possible mechanisms, diagnosis methods and current treatment of bone metastasis in lung cancer. We divided the process of bone metastasis in lung cancer into three steps: tumor invasion, tumor cell migration and invasion in bone tissue. It may be influenced by genetic factors, microenvironment and other adhesion-related factors. Imaging examination, laboratory examination, and pathological examination are used to diagnose lung cancer metastasis to bone. Surgery, radiotherapy, targeted therapy, bisphosphonate, radiation therapy and chemotherapy are the common clinical treatment methods currently. We also found some problems remained to be solved. For example, drugs for skeletal related events mainly target on osteoclasts at present, which increase the ratio of patients in osteoporosis and fractures in the long term. In all, this review provides the direction for future research on bone metastasis in lung cancer.

Objective: To accurately evaluate tumor heterogeneity, make multidimensional diagnosis according to the causes and phenotypes of tumor heterogeneity, and assist in the individualized treatment of tumors.Background: Tumor heterogeneity is one of the most essential characteristics of malignant tumors. In tumor recurrence, development, and evolution, tumor heterogeneity can lead to the formation of different cell groups with other molecular characteristics. Tumor heterogeneity can be characterized by the uneven distribution of tumor cell subsets of other genes between and within the disease site (spatial heterogeneity) or the time change of cancer cell molecular composition (temporal heterogeneity). The discovery of tumor targeting drugs has dramatically promoted tumor therapy. However, the existence of heterogeneity seriously affects the effect of tumor treatment and the prognosis of patients. Methods:The literature discussing tumor heterogeneity and its resistance to tumor therapy was broadly searched to analyze tumor heterogeneity as well as the challenges and solutions for gene detection and tumor drug therapy. Conclusions:Tumor heterogeneity is affected by many factors consist of internal cell factors and cell microenvironment. Tumor heterogeneity greatly hinders effective and individualized tumor treatment.Understanding the fickle of tumors in multiple dimensions and flexibly using a variety of detection methods to capture the changes of tumors can help to improve the design of diagnosis and treatment plans for cancer and benefit millions of patients.

No abstract

Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co‐analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co‐analysed. Further, differential enriched pathways were identified by GSEA. In sum, a scientific hypothesis for recurrence LGG including TF, immune gene and immune cell/pathway was established. In our study, a total of 536 primary LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differentials expressed TFs were identified. A total of 106 DEIGs were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and P value of Kaplan‐Meier curve < 0.001. GATA6 (TF) and COL3A1 (DEIG) were selected (R = 0.900, P < 0.001, positive) as significant TF‐immune gene links. Type II IFN response (P < 0.001) was the significant immune pathway. Propanoate metabolism (P < 0.001) was the significant KEGG pathway. We proposed that COL3A1 was positively regulated by GATA6, and by effecting type II IFN response and propanoate metabolism, COL3A1 involved in LGG recurrence.

Lung cancer remains the leading cause of cancer‐related death worldwide. Lung adenocarcinoma (LUAD) is thought to be caused by precursor lesions of atypical adenoma‐like hyperplasia and may have extensive in situ growth before infiltration. To explore the relevant factors in heterogeneity and evolution of lung adenocarcinoma subtypes, the authors perform single‐cell RNA sequencing (scRNA‐seq) on tumor and normal tissue from five multiple nodules’ LUAD patients and conduct a thorough gene expression profiling of cancer cells and cells in their microenvironment at single‐cell level. This study gives a deep understanding of heterogeneity and evolution in early glandular neoplasia of the lung. This dataset leads to discovery of the changes in the immune microenvironment during the development of LUAD, and the development process from adenocarcinoma in situ (AIS) to invasive adenocarcinoma (IAC). This work sheds light on the direction of early tumor development and whether they are homologous.

BackgroundImmunotherapies may prolong the survival of patients with small-cell lung cancer (SCLC) to some extent. The role of forkhead box protein P3 (FOXP3) in tumor microenvironment (TME) remains controversial. We aimed to examine FOXP3-related expression characteristics and prognostic values and to develop a clinically relevant predictive system for SCLC.MethodsWe enrolled 102 patients with histologically confirmed SCLC at stages I–III. Through immunohistochemistry, we determined the expression pattern of FOXP3 and its association with other immune biomarkers. By machine learning and statistical analysis, we constructed effective immune risk score models. Furthermore, we examined FOXP3-related enrichment pathways and TME traits in distinct cohorts.ResultsIn SCLC, FOXP3 level was significantly associated with status of programmed death-ligand 1 (PD-L1), programmed cell death protein 1 (PD-1), CD4, CD8, and CD3 (p=0.002, p=0.001, p=0.002, p=0.030, and p<0.001). High FOXP3 expression showed longer relapse-free survival (RFS) than the low-level group (41.200 months, 95% CI 26.937 to 55.463, vs 14.000 months, 95% CI 8.133 to 19.867; p=0.008). For tumor-infiltrating lymphocytes (TILs), subgroup analysis demonstrated FOXP3 and PD-1, PD-L1, lymphocyte activation gene-3, CD3, CD4, or CD8 double positive were significantly correlated with longer RFS. We further performed importance evaluation for immune biomarkers, constructed an immune risk score incorporating the top three important biomarkers, FOXP3, TIL PD-L1, and CD8, and found their independently prognostic role to predict SCLC relapse. Better predictive performance was achieved in this immune risk model compared with single-indicator-based or two-indicator-based prediction systems (area under the curve 0.715 vs 0.312–0.711). Then, relapse prediction system integrating clinical staging and immune risk score was established, which performed well in different cohorts. High FOXP3-related genes were enriched in several immune-related pathways, and the close relationships of interleukin-2, CD28, basic excision repair genes MUTYH, POLD1, POLD2, and oxidative phosphorylation related gene cytochrome c oxidase subunit 8A with FOXP3 expression were revealed. Moreover, we found low-immune risk score group had statistically higher activated CD4+ memory T cells (p=0.014) and plasma cells (p=0.049) than the high-risk group. The heterogeneity of tumor-infiltrating immune cells might represent a promising feature for risk prediction in SCLC.ConclusionFOXP3 interacts closely with immune biomarkers on tumor-infiltrating cells in TME. This study highlighted the crucial prognostic value and promising clinical applications of FOXP3 in SCLC.

Background: It has been proven that the treatment window of small cell lung cancer (SCLC) is short, so it is vital to find other possible therapeutic targets. CD39 inhibits natural killer (NK) cells and promotes the occurrence and metastasis of tumors. There has been little research about the role of CD39 in SCLC, so we explored the correlation between CD39 and other surface antigens, and its association with survival in SCLC.Methods: This study included 75 patients with SCLC from Shanghai Pulmonary Hospital. After paraffin embedding and sectioning, immunohistochemistry (IHC) was applied. Then we identify cutoff value for CD39 and other surface antigens based on the analysis of ROC curve in RFS by SPSS. All statistical analyses were based on SPSS and Graphpad Prism8. Chi-square test, Kendall's tau-b correlation analysis, Logistic regression analysis, Kaplan-Meier method, univariate and multivariate Cox regression analysis were conducted. In all analyses, P = 0.05 distinguished whether they had statistical significance. Results: Of the 75 SCLC patients enrolled in this study, 61.33% positively expressed CD39. A correlation between CD39 and programmed cell death-ligand 1 (PD-L1) (P=0.007), CD3 (P<0.001), CD4 (P<0.001), CD8 (P<0.001), and forkhead box P3 (FOXP3) (P<0.001) on tumor-infiltrating lymphocytes (TILs) was identified by correlation analysis and logistic regression analysis. Based on Kaplan-Meier survival analysis, we found that CD39 affected relapse-free survival (RFS) [negative vs. positive, 95% confidence interval (CI): 0.2765-0.9862, P=0.0390]. SCLC patients with high-expressed CD39 and low-expressed PD-L1 had poor prognosis (P<0.001). Positive expression of CD39 and negative expression of CD3, CD4, CD8, and FOXP3 also indicated shorter RFS (P=0.0409). Univariate and multivariate Cox regression analysis was performed to confirm the factors that influenced RFS. Conclusions: CD39, programmed cell death-1 (PD-1), and PD-L1 expressed on TILs but not on tumor cells. CD39 has a significant association with PD-L1, CD3, CD4, CD8, and FOXP3 on TILs. The positive expression of CD39 predicts poor prognosis. SCLC patients with low expression of CD39 combined with high expression of PD-L1 or CD3, CD4, CD8, and FOXP3 have a more favorable prognosis.

Purpose To investigate the changes of retinal nerve fiber layer (RNFL) in patients after an attack of primary acute angle closure glaucoma (PAACG) and to assess the impact of attack time on prognosis of retinal changes. Design cross-sectional study. Methods Twenty-six patients with unilateral PAACG attack and cataracts from 2017 to 2019 were enrolled. Eyes with PAACG attack time less than 1 day constituted the group A (n = 13), while eyes with PAACG attack time more than 1 day constituted the group B (n = 13). All patients received phacoemulsification and viscogoniosynechialysis after intraocular pressure (IOP) lowering. All patients underwent ophthalmic examinations including IOP, best-corrected visual acuity (BCVA), and visual field (VF). Optical coherence tomography angiography (OCTA) was used to obtain circumpapillary RNFL vessel density (cpVD). Spectral domain optical coherence tomography (SD-OCT) was used to examine the peripapillary RNFL and macular ganglion cell complex (GCC). All patients accepted 2 assessments before and 1 month after the procedure. Results The IOP of all patients recovered to normal (12.77 ± 2.65 mm Hgvs. 12.77 ± 3.85 mmHg, p=0.834) after the procedure. Patients in the group A had better BCVA improvement than those in the group B (1.32 ± 0.84 vs. 0.50 ± 0.21, p=0.004), as well as better mean defect (MD) values from VF (−3.65 ± 2.54 vs −16.05 ± 5.99, p < 0.001). Compared with group B, patients in the group A had thicker macula (Fovea area: 255.00 ± 27.94 μm vs. 203.92 ± 59.73 μm, p=0.010), thicker GCC (82.62 ± 8.76 μm vs. 65.23 ± 18.56 μm, p=0.005), and thicker RNFL (105.08 ± 9.38 μm vs. 77.69 ± 20.23 μm, p < 0.001). Higher blood flow density in all-plexus peripapillary retina was observed in the group A eyes compared with group B (full sector: 0.56 ± 0.02 vs. 0.41 ± 0.07, p < 0.001). In both groups, the association between average RNFL thickness and cpVD as well as MD values and pattern standard deviation (PSD) values from VF was stronger (R2 = 0.58, 0.60, −0.54, respectively, all p < 0.001) than the association between GCC thickness and cpVD, as well as MD values and PSD values (R2 = 0.37, p=0.001; R2 = 0.37, p=0.001; R2 = −0.27, p=0.007). Conclusion Patients with attack time less than 1 day had better retinal thickness and all-plexus peripapillary retina blood flow density. Controlling the attack time could decrease retinal damage by PAACG.