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.
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.
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.
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