RESUMEN
The ferritin-heavy chain (FTH1) is the catalytic subunit of the ferroxidase ferritin, which prevents oxidative DNA damage via intracellular iron storage. FTH1 was shown to be a prognostic marker for triple-negative breast cancer (BC) patients and associated with an enrichment of CD8+ effector T cells. However, whether the expression and localization of FTH1 are also associated with clinical outcome in other BC subtypes is unknown. Here, we investigated the association of FTH1 with time to survival in BCs from 222 BRCA1/2 mutation carriers by immunohistochemistry on tissue microarrays. In addition, for 51 of these patients, the association between FTH1 and specific subsets of T cells was evaluated on whole slides using automatic scoring algorithms. We revealed that nuclear FTH1 (nFTH1) expression, in multivariable analyses, was associated with a shorter disease-free (HR = 2.71, 95% CI = 1.49-4.92, p = 0.001) and metastasis-free survival (HR = 3.54, 95% CI = 1.45-8.66, p = 0.006) in patients carrying a BRCA1/2 mutation. However, we found no relation between cytoplasmic FTH1 expression and survival of BRCA1/2 mutation carriers. Moreover, we did not detect an association between FTH1 expression and the amount of CD45+ (p = 0.13), CD8+ (p = 0.18), CD4+ (p = 0.20) or FOXP3+ cells (p = 0.17). Consequently, the mechanism underlying the worse recurrence-free survival of nFTH1 expression in BRCA1/2 mutation carriers needs further investigation.
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Patients with cancers that are deficient for homologous recombination repair (HRD) may benefit from PARP inhibitor treatment. Therefore, methods that identify such cancers are crucial. Using whole genome sequencing data, specific genomic scars derived from somatic mutations and genomic rearrangements can identify HRD tumors, with only BRCA1-like HRD cancers profoundly displaying small (<10 kb) tandem duplications (TDs). In this manuscript we describe a method of detecting BRCA1-type HRD in breast cancer (BC) solely from RNA sequencing data by identifying TDs surfacing in transcribed genes. We find that the number of identified TDs (TD-score) is significantly higher in BRCA1-type vs. BRCA2-type BCs, or vs. HR-proficient BCs (p = 2.4 × 10-6 and p = 2.7 × 10-12, respectively). A TD-score ≥2 shows an 88.2% sensitivity (30 out of 34) to detect a BRCA1-type BC, with a specificity of 64.7% (143 out of 221). Pathway enrichment analyses showed genes implicated in cancer to be affected by TDs of which PTEN was found significantly more frequently affected by a TD in BRCA1-type BC. In conclusion, we here describe a novel method to identify TDs in transcripts and classify BRCA1-type BCs with high sensitivity.
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BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a life-threatening lung disorder with an unknown aetiology. The roles of long non-coding RNAs (lncRNAs) and its related competing endogenous RNAs (ceRNA) network in IPF remains poorly understood. In this study, we aimed to build a lncRNA-miRNA-mRNA network and explore the pathogenesis of IPF. METHODS: We screened differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between IPF and control lung tissues from two datasets. The ceRNA network was built according to the interactions between DElncRNA, miRNA, and DEmRNA. Functional enrichment analysis of DemRNAs was performed using Metascape. CIBERSORT (Cell type Identification by Estimating Relative Subsets Of known RNA Transcripts) was applied to estimate the fraction of 22 immune cells in IPF and controls lung tissue samples. Then we investigated the correlation between immune cells and clinical traits. RESULTS: We constructed a lncRNA-miRNA-mRNA network, which was composed of two DElncRNAs, 18 miRNAs, 66 DemRNAs. Functional enrichment analysis showed that the DEmRNAs mainly participated in MicroRNAs in cancer. By applying CIBERSORT, we found that IPF tissue samples had a higher proportion of plasma cells, resting mast cells and a lower proportion of resting NK cells, monocytes, neutrophils compared with control tissue samples. Also, our results indicated that immune cells were associated with the severity of IPF. CONCLUSIONS: In summary, this is the first study to build lncRNA-miRNA-mRNA ceRNA network of IPF, which may improve our understanding of IPF pathogenesis. Our study indicates that immune cells in lung tissues may predict disease severity and participate in the development of IPF. Future prospective studies are required to confirm the findings of the current study.
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BACKGROUND Growing evidence shows that the tumor microenvironment plays a crucial role in the pathogenesis of hepatocellular carcinoma (HCC). The present work aimed to screen tumor microenvironment-related genes strongly related to prognosis and to construct a prognostic gene expression model for HCC. MATERIAL AND METHODS We downloaded gene expression data of 371 HCC patients in The Cancer Genome Atlas (TCGA). A novel ESTIMATE algorithm was applied to calculate immune scores and stromal scores for each patient. Then, the differentially-expressed genes (DEGs) were detected according to the immune and stromal scores, and tumor microenvironment-related genes were further explored. Univariate, Lasso, and multivariate Cox analyses were performed to build the tumor microenvironment-related prediction model. RESULTS Stromal and immune scores were calculated and were found to be correlated with the 3-year prognosis of HCC patients. DEGs were detected according to the stromal and immune scores. There were 49 genes with prognostic value in both TCGA and ICGC (International Cancer Genome Consortium) considered as prognostic tumor microenvironment-related genes. Univariate, Lasso, and multivariate Cox analyses were conducted. A novel 2-gene signature (IL18RAP and GPR182) was built for HCC 3-year prognosis prediction. The 2-gene signature was regarded as an independent prognostic predictor that was correlated with 3-year survival rate, as shown by Cox regression analysis. CONCLUSIONS This study offers a novel 2-gene signature to predict overall survival of patients with HCC, which has the potential to be used as an independent prognostic predictor. Overall, this study reveals more details about the tumor microenvironment in HCC and offers novel candidate biomarkers.