Gene Expression Profiling for Diagnosis of Triple-Negative Breast Cancer: A Multicenter, Retrospective Cohort Study

Background: Triple-negative breast cancer (TNBC) accounts for 12–20% of all breast cancers. Diagnosis of TNBC is sometimes quite difficult based on morphological assessment and immunohistochemistry alone, particularly in the metastatic setting with no prior history of breast cancer.Methods: Molecular profiling is a promising diagnostic approach that has the potential to provide an objective classification of metastatic tumors with unknown primary. In this study, performance of a novel 90-gene expression signature for determination of the site of tumor origin was evaluated in 115 TNBC samples. For each specimen, expression profiles of the 90 tumor-specific genes were analyzed, and similarity scores were obtained for each of the 21 tumor types on the test panel. Predicted tumor type was compared to the reference diagnosis to calculate accuracy. Furthermore, rank product analysis was performed to identify genes that were differentially expressed between TNBC and other tumor types.Results: Analysis of the 90-gene expression signature resulted in an overall 97.4% (112/115, 95% CI: 0.92–0.99) agreement with the reference diagnosis. Among all specimens, the signature correctly classified 97.6% of TNBC from the primary site (41/42) and lymph node metastasis (41/42) and 96.8% of distant metastatic tumors (30/31). Furthermore, a list of genes, including AZGP1, KRT19, and PIGR, was identified as differentially expressed between TNBC and other tumor types, suggesting their potential use as discriminatory markers.Conclusion: Our results demonstrate excellent performance of a 90-gene expression signature for identification of tumor origin in a cohort of both primary and metastatic TNBC samples. These findings show promise for use of this novel molecular assay to aid in differential diagnosis of TNBC, particularly in the metastatic setting

Classification of colon adenocarcinoma based on immunological characterizations: Implications for prognosis and immunotherapy

Accurate immune molecular typing is pivotal for screening out patients with colon adenocarcinoma (COAD) who may benefit from immunotherapy and whose tumor microenvironment (TME) was needed for reprogramming to beneficial immune-mediated responses. However, little is known about the immune characteristic of COAD. Here, by calculating the enrichment score of immune characteristics in three online COAD datasets (TCGA-COAD, GSE39582, and GSE17538), we identified 17 prognostic-related immune characteristics that overlapped in at least two datasets. We determined that COADs could be stratified into three immune subtypes (IS1–IS3), based on consensus clustering of these 17 immune characteristics. Each of the three ISs was associated with distinct clinicopathological characteristics, genetic aberrations, tumor-infiltrating immune cell composition, immunophenotyping (immune “hot” and immune “cold”), and cytokine profiles, as well as different clinical outcomes and immunotherapy/therapeutic response. Patients with the IS1 tumor had high immune infiltration but immunosuppressive phenotype, IS3 tumor is an immune “hot” phenotype, whereas those with the IS2 tumor had an immune “cold” phenotype. We further verified the distinct immune phenotype of IS1 and IS3 by an in-house COAD cohort. We propose that the immune subtyping can be utilized to identify COAD patients who will be affected by the tumor immune microenvironment. Furthermore, the ISs may provide a guide for personalized cancer immunotherapy and for tumor prognosis

Structure-aware deep model for MHC-II peptide binding affinity prediction

Abstract The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas

Abstract Background Renal cancers account for more than 3% of all adult malignancies and cause more than 23,400 deaths per year in China alone. The four most common types of kidney tumours include clear cell, papillary, chromophobe and benign oncocytoma. These histological subtypes vary in their clinical course and prognosis, and different clinical strategies have been developed for their management. Some kidney tumours can be very difficult to distinguish based on the pathological assessment of morphology and immunohistochemistry. Methods Six renal cell carcinoma microarray data sets, including 106 clear cell, 66 papillary, 42 chromophobe, 46 oncocytoma and 35 adjacent normal tissue samples, were subjected to integrative analysis. These data were combined and used as a training set for candidate gene expression signature identification. In addition, two independent cohorts of 1020 RNA-Seq samples from The Cancer Genome Atlas database and 129 qRT-PCR samples from Fudan University Shanghai Cancer Center (FUSCC) were analysed to validate the selected gene expression signature. Results A 44-gene expression signature derived from microarray analysis was strongly associated with the histological differentiation of renal tumours and could be used for tumour subtype classification. The signature performance was further validated in 1020 RNA-Seq samples and 129 qRT-PCR samples with overall accuracies of 93.4 and 93.0%, respectively. Conclusions A 44-gene expression signature that could accurately discriminate renal tumour subtypes was identified in this study. Our results may prompt further development of this gene expression signature into a molecular assay amenable to routine clinical practice

The polycomb group protein EZH2 induces epithelial–mesenchymal transition and pluripotent phenotype of gastric cancer cells by binding to PTEN promoter

Abstract Background The influences of oncogenic Ezh2 on the progression and prognosis of gastric cancer (GC) and the underlying mechanisms are still poorly understood. Here, we aimed at investigating clinicopathological significance of Ezh2 in GC and the mechanisms underlying its function in GC development. Methods The expression level of Ezh2 was determined by qRT-PCR, immunoblot, and immunohistochemistry analysis in 156 pairs of GC tissues and adjacent normal gastric mucosa tissues. The biological functions of Ezh2 were assessed by in vitro and in vivo functional experiments. Chromatin immunoprecipitation (ChIP), luciferase, and Western blotting analyses were utilized to identify the relationship between Ezh2 and the PTEN/Akt signaling. Results The expression of Ezh2 was higher in gastric cancer tissues in comparison with para-nontumorous epithelium. High expression of Ezh2 was associated with more aggressive biological behavior and poor prognosis in GC. In vitro studies indicated that Ezh2 promoted GC cells’ proliferation and clonogenicity. Besides, Ezh2 led to the acquisition of epithelial–mesenchymal transition (EMT) phenotype of GC cells and enhanced GC cell migration and invasion capacity. In particular, Ezh2 strengthened sphere-forming capacity of GC cells, indicating its role in the enrichment of GC stem cells. Furthermore, we found that PTEN/Akt signaling contributed to the effects of Ezh2 on cancer stem cells (CSC) and EMT phenotype in GC cells, and blocking PTEN signaling significantly rescued the effects of Ezh2. Conclusions Taken together, Ezh2 has a central role in regulating diverse aspects of the pathogenesis of GC in part by involving PTEN/Akt signaling, indicating that it could be an independent prognostic factor and potential therapeutic target

The lncRNA NEAT1 activates Wnt/β-catenin signaling and promotes colorectal cancer progression via interacting with DDX5

Abstract Background The long noncoding RNA nuclear-enriched abundant transcript 1 (NEAT1) has been reported to be overexpressed in colorectal cancer (CRC). However, its underlying mechanisms in the progression of CRC have not been well studied. Methods To investigate the clinical significance of NEAT1, we analyzed its expression levels in a publicly available dataset and in 71 CRC samples from Fudan University Shanghai Cancer Center. Functional assays, including the CCK8, EdU, colony formation, wound healing, and Transwell assays, were used to determine the oncogenic role of NEAT1 in human CRC progression. Furthermore, RNA pull-down, mass spectrometry, RNA immunoprecipitation, and Dual-Luciferase Reporter Assays were used to determine the mechanism of NEAT1 in CRC progression. Animal experiments were used to determine the role of NEAT1 in CRC tumorigenicity and metastasis in vivo. Results NEAT1 expression was significantly upregulated in CRC tissues compared with its expression in normal tissues. Altered NEAT1 expression led to marked changes in proliferation, migration, and invasion of CRC cells both in vitro and in vivo. Mechanistically, we found that NEAT1 directly bound to the DDX5 protein, regulated its stability, and sequentially activated Wnt signaling. Our study showed that NEAT1 indirectly activated the Wnt/β-catenin signaling pathway via DDX5 and fulfilled its oncogenic functions in a DDX5-mediated manner. Clinically, concomitant NEAT1 and DDX5 protein levels negatively correlated with the overall survival and disease-free survival of CRC patients. Conclusions Our findings indicated that NEAT1 activated Wnt signaling to promote colorectal cancer progression and metastasis. The NEAT1/DDX5/Wnt/β-catenin axis could be a potential therapeutic target of pharmacological strategies

Evolutionary Trajectories of Primary and Metastatic Pancreatic Neuroendocrine Tumors Based on Genomic Variations

Liver metastases are common in pancreatic neuroendocrine tumors (PanNETs) patients and they are considered a poor prognostic marker. This study aims to analyze the spatiotemporal patterns of genomic variations between primary and metastatic tumors, and to identify the key related biomolecular pathways. We performed next-generation sequencing on paired tissue specimens of primary PanNETs (n = 11) and liver metastases (n = 12). Low genomic heterogeneity between primary PanNETs and liver metastases was observed. Genomic analysis provided evidence that polyclonal seeding is a prevalent event during metastatic progression, and may be associated with the progression-free survival. Besides this, copy number variations of BRCA1/BRCA2 seem to be associated with better prognosis. Pathways analysis showed that pathways in cancer, DNA repair, and cell cycle regulation-related pathways were significantly enriched in primary PanNETs and liver metastases. The study has shown a high concordance of gene mutations between the primary tumor and its metastases and the shared gene mutations may occur during oncogenesis and predates liver metastasis, suggesting an earlier onset of metastasis in patients with PanNETs, providing novel insight into genetic changes in metastatic tumors of PanNETs

Additional file 1 of Long-read transcriptome landscapes of primary and metastatic liver cancers at transcript resolution

Supplementary Material

Additional file 1: of The polycomb group protein EZH2 induces epithelial–mesenchymal transition and pluripotent phenotype of gastric cancer cells by binding to PTEN promoter

Materials and Methods. Table S1. Relationship between EZH2 expression and clinicopathologic parameters of gastric cancer patients. Table S2. Univariate and multivariate analysis of clinicopathological factors for disease-free survival in gastric cancer (qRT-PCR cohort). Table S3. Univariate and multivariate analysis of clinicopathological factors for overall survival in gastric cancer (qRT-PCR cohort). Table S4. Univariate and multivariate analysis of clinicopathological factors for overall survival in gastric cancer (IHC cohort). Table S5. Correlation analysis of expression of stem cell related factors with EZH2. Table S6. Primers and siRNA sequences used in this study. (DOCX 67 kb