Lung cancer: new therapeutic targets, new definitions.
Ontology highlight
ABSTRACT:
SUBMITTER: Finigan JH
PROVIDER: S-EPMC3400992 | biostudies-literature | 2012 Jul
REPOSITORIES: biostudies-literature
Publications
Lung cancer: new therapeutic targets, new definitions.
Finigan James H JH Kern Jeffrey A JA
American journal of respiratory and critical care medicine 20120701 1
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Project description:Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein-protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein-protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients' prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.
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Project description:Lung cancer is still the leading cause of cancer death worldwide. Both histologically and molecularly lung cancer is heterogeneous. This review summarizes the current knowledge of the pathways involved in the various types of lung cancer with an emphasis on the clinical implications of the increasing number of actionable molecular targets. It describes the major pathways and molecular alterations implicated in the development and progression of non-small cell lung cancer (adenocarcinoma and squamous cancer), and of small cell carcinoma, emphasizing the molecular alterations comprising the specific blueprints in each group. The approved and investigational targeted therapies as well as the immune therapies, and clinical trials exploring the variety of targeted approaches to treatment of lung cancer are the main focus of this review.
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Project description:UnlabelledHere, we report that novel epidermal growth factor receptor (EGFR) gene fusions comprising the N-terminal of EGFR linked to various fusion partners, most commonly RAD51, are recurrent in lung cancer. We describe five patients with metastatic lung cancer whose tumors harbored EGFR fusions, four of whom were treated with EGFR tyrosine kinase inhibitors (TKI) with documented antitumor responses. In vitro, EGFR-RAD51 fusions are oncogenic and can be therapeutically targeted with available EGFR TKIs and therapeutic antibodies. These results support the dependence of EGFR-rearranged tumors on EGFR-mediated signaling and suggest several therapeutic strategies for patients whose tumors harbor this novel alteration.SignificanceWe report for the first time the identification and therapeutic targeting of EGFR C-terminal fusions in patients with lung cancer and document responses to the EGFR inhibitor erlotinib in 4 patients whose tumors harbored EGFR fusions. Findings from these studies will be immediately translatable to the clinic, as there are already several approved EGFR inhibitors. Cancer Discov; 6(6); 601-11. ©2016 AACR.See related commentary by Paik, p. 574This article is highlighted in the In This Issue feature, p. 561.
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Project description:Despite the treatment of lung adenocarcinoma (LUAD) having partially improved in recent years, LUAD patients still have poor prognosis rates. Therefore, it is especially important to explore effective biomarkers and exploit novel therapeutic developments. High-throughput technologies are widely used as systematic approaches to explore differences in expressions of thousands of genes for both biological and genomic systems. Recently, using big data analyses in biomedicine research by integrating several high-throughput databases and tools, including The Cancer Genome Atlas (TCGA), cBioportal, Oncomine, and Kaplan-Meier plotter, is an important strategy to identify novel biomarkers for cancer therapy. Here, we used two different comprehensive bioinformatics analysis and revealed protein tyrosine phosphatase non-receptor type (PTPN) family genes, especially PTPN1 and PTPN22, were downregulated in lung cancer tissue in comparison with normal samples. The survival curves indicated that LUAD patients with high transcription levels of PTPN5 were significantly associated with a good prognosis. Meanwhile, Gene Ontology (GO) and MetaCore analyses indicated that co-expression of the PTPN1, PTPN5, and PTPN21 genes was significantly enriched in cancer development-related pathways, including GTPase activity, regulation of small GTPase-mediated signal transduction, response to mechanical stimuli, vasculogenesis, organ morphogenesis, regulation of stress fiber assembly, mitogen-activated protein kinase (MAPK) cascade, cell migration, and angiogenesis. Collectively, this study revealed that PTPN family members are both significant prognostic biomarkers for lung cancer progression and promising clinical therapeutic targets, which provide new targets for treating LUAD patients.
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Project description:Esophageal cancer is one of the most common gastrointestinal malignant diseases and there is still no effective treatment. The incidence of esophageal cancer in the world is relatively high and on the increase year by year. Thus, the elaboration on the carcinogenesis of esophageal cancer and the identification of new biomarkers and therapeutic targets is quite beneficial to optimizing the current therapeutic regimen for treating such deadly disease. More and more evidence has shown that non-coding RNAs play an important role in the development and progression of multiple human cancers, including esophageal cancer. microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two functional kinds of non-coding RNAs that have been well investigated. They exert tumor suppressive or promoting effect by specifically regulating the expression of certain downstream target genes, which is tumor specific. It is also proved that miRNAs and lncRNAs level in tissue and plasma from esophageal cancer patients are closely correlated with the survival and disease progression, which could be used as a prognostic factor and therapeutic target for esophageal cancer.
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Project description:The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.
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Project description:We aimed to identify new targets affecting gastric cancer (GC) prognosis. Six target genes were identified from hub genes based on their relationship with important factors affecting tumor progression, like immune infiltration, purity, tumor mutation burden (TMB), and tumor microenvironment (TME) score. The effect of target genes' somatic mutations and copy number alteration (CNA) was examined to determine their effect on GC prognosis. Six target genes (FBN1, FN1, HGF, MMP9, THBS1, and VCAN) were identified. Reduced expression of each target gene, except MMP9, indicated better prognosis and lower grade in GC. FBN1, THBS1, and VCAN showed lower expression in stage I GC. Non-silencing mutations of the six genes played a role in significantly higher TMB and TME scores. THBS1 mutation was associated with earlier stage GC, and VCAN mutation was associated with lower grade GC. However, patients with target gene CNA displayed higher tumor purity. MMP9, THBS1, and VCAN CNA was associated with lower grade GC, while FBN1 CNA reflected earlier T stage. Additionally, the target genes may affect GC prognosis by influencing multiple oncogenic signaling pathways. FBN1, FN1, HGF, MMP9, THBS1, and VCAN may be new GC prognostic targets by affecting tumor purity, TMB, TME score, and multiple oncogenic signaling pathways.
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Project description:The aim of the present study was to identify potential therapeutic targets for lung cancer and explore underlying molecular mechanisms of its development and progression. The gene expression profile datasets no. GSE3268 and GSE19804, which included five and 60 pairs of tumor and normal lung tissue specimens, respectively, were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between lung cancer and normal tissues were identified, and gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the DEGs was performed. Furthermore, protein‑protein interaction (PPI) networks and a transcription factor (TF) regulatory network were constructed and key target genes were screened. A total of 466 DEGs were identified, and the PPI network indicated that IL‑6 and MMP9 had key roles in lung cancer. A PPI module containing 34 nodes and 547 edges was obtained, including PTTG1. The TF regulatory network indicated that TFs of FOSB and LMO2 had a key role. Furthermore, MMP9 was indicated to be the target of FOSB, while PTTG1 was the target of LMO2. In conclusion, the bioinformatics analysis of the present study indicated that IL‑6, MMP9 and PTTG1 may have key roles in the progression and development of lung cancer and may potentially be used as biomarkers or specific therapeutic targets for lung cancer.
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