Inherited mutations in BRCA1 confer susceptibility to breast and ovarian neoplasms. However, the function of BRCA1 and the role of BRCA1 in noninherited cancer remain unknown. Characterization of alternately spliced forms of BRCA1 may identify functional regions; thus, we constructed expression vectors of BRCA1 and a splice variant lacking exon 11, designated BRCA1⌬672-4095. Immunofluorescence studies indicate nuclear localization of BRCA1 but cytoplasmic localization of BRCA1⌬672-4095. Two putative nuclear localization signals (designated NLS1 and NLS2) were identified in exon 11; immunofluorescence studies indicate that only NLS1 is required for nuclear localization. RNA analysis indicates the expression of multiple, tissue-specific forms of BRCA1 RNAs; protein analysis with multiple antibodies suggests that at least three BRCA1 isoforms are expressed, including those lacking exon 11. The results suggest that BRCA1 is a nuclear protein and raise the possibility that splicing is one form of regulation of BRCA1 function by alteration of the subcellular localization of expressed proteins.BRCA1 was isolated by positional cloning methods as a gene linked to breast cancer in families with a pattern of autosomal dominant inheritance of the disease (21). Single dominant susceptibility alleles are thought to account for 5 to 10% of all breast cancers, and BRCA1 germ line mutations are widely held to be responsible for approximately 50% of all inherited breast cancers. Inherited BRCA1 mutations are also thought to be responsible for the disease in 80 to 90% of all families with breast-ovarian cancer syndrome (9). Women inheriting mutations in BRCA1, most of which are truncating mutations that result in nonfunctional or unstable proteins (8), have an 85% chance of developing breast cancer in their lifetime (9). An on-line database now provides a listing of known BRCA1 mutations (http:// www. nchgr. nih. gov/ Intramural_research/ Lab_transfer/Bic/index.html), but little is known about the regulation of this gene or the function of its protein product. Unfortunately, mutations in BRCA1 are distributed evenly over the gene, providing little in the way of clues for localizing critical functional regions.BRCA1 is a large gene, with a coding region of 5.5 kb and a total mRNA of approximately 8.0 kb. There is little identifiable homology to known genes, with the exception of a short region in the 5Ј end (spanning exons 2 to 5) that encodes a RING finger with a typical Cys 3 -His-Cys 4 structure. This is a zincbinding motif that is found in a family of transcription factors and may be a protein-protein interaction site. Initial reports also provided evidence for a complex pattern of alternate splicing and the potential for translation of a number of BRCA1 protein isoforms (21). BRCA1 fits the model of a classic tumor suppressor gene, a hypothesis supported by recent work demonstrating that expression of BRCA1 inhibits growth of breast and ovarian cancer cell lines and MCF7-based tumor development in nude mice (16). Additional da...

It has been generally believed that cancer-associated fibroblasts (CAFs) have the ability to increase the process of tumor angiogenesis. However, the potential mechanisms by which cancer-derived exosomes in lung cancer (LC) remains to be investigated. LC-derived exosomes were administrated to NIH/3T3 cells. A variety of experiments were conducted to investigate the proangiogenic factors of CAFs, including Western blot, RT-PCR, colony formation assay, tube formation assay, Matrigel plug assay et al. In addition, the impact of JAK2/STAT3 signaling pathway were also explored. The role of hsa-miR-210 was identified with microarray profiling and validated in vitro and in vivo assays. The target of miR-210 was screened by RNA pull down, RNA-sequencing and then verified. It was shown that LC-derived exosomes could induce cell reprogramming, thus promoting the fibroblasts transferring into CAFs. In addition, the exosomes with overexpressed miR-210 could increase the level of angiogenesis and vice versa, which suggested the miR-210 secreted by the LC-derived exosomes may initiate the CAF proangiogenic switch. According to our analysis, the miR-210 had the ability of elevating the expression of some proangiogenic factors such as MMP9, FGF2 and vascular endothelial growth factor (VEGF) a (VEGFa) by activating the JAK2/STAT3 signaling pathway, ten-eleven translocation 2 (TET2) was identified as the target of miR-210 in CAFs which has been involved in proangiogenic switch. miR-210 was overexpressed in serum exosomes of untreated non-small cell LC (NSCLC) patients. We concluded that the promotion effect of exosomal miR-210 on proangiogenic switch of CAFs may be explained by the modulation of JAK2/STAT3 signaling pathway and TET2 in recipient fibroblasts.

Background Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD). Methods RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method. Results A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance ( P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis. Conclusions We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

Background Long non-coding RNAs (lncRNAs) are increasingly recognized as the crucial mediators in the regulation of ferroptosis and iron metabolism. A systematic understanding of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in lung adenocarcinoma (LUAD) is essential for new diagnostic and therapeutic strategies. Methods FIRLs were obtained through Pearson correlation analysis between ferroptosis and iron-metabolism related genes and all lncRNAs. Univariate and multivariate Cox regression analysis were used to identify optimal prognostic lncRNAs. Next, a novel signature was constructed and risk score of each patient was calculated. Survival analysis and ROC analysis were performed to evaluate the predictive performance using The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus (GEO) datasets, respectively. Furthermore, multivariate Cox and stratification analysis were used to assess prognostic value of this signature in whole cohort and various subgroups. The correlation of risk signature with immune infiltration and gene mutation was also discussed. The expression of lncRNAs was verified by quantitative real-time PCR (qRT-PCR). Results A 7-FIRLs signature including ARHGEF26-AS1, LINC01137, C20orf197, MGC32805, TMPO-AS1, LINC00324, and LINC01116 was established in the present study to assess the overall survival (OS) of LUAD. The survival analysis and ROC curve indicated good predictive performance of the signature in both the TCGA training set and the GEO validation set. Multivariate Cox and stratification analysis indicated that the 7‐FIRLs signature was an independent prognostic factor for OS. Nomogram exhibited robust validity in prognostic prediction. Differences in immune cells, immune functions and gene mutation were also found between high-risk and low-risk groups. Conclusions This risk signature based on the FIRLs may be promising for the clinical prediction of prognosis and immunotherapeutic responses in LUAD patients.

BackgroundTumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).Methods“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.ResultsA total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.ConclusionIn conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.