Luminal androgen receptor subtype and tumor-infiltrating lymphocytes groups based on triple-negative breast cancer molecular subclassification

In our previous study, we developed a triple-negative breast cancer (TNBC) subtype classification that correlated with the TNBC molecular subclassification. In this study, we aimed to evaluate the predictor variables of this subtype classification on the whole slide and to validate the model's performance by using an external test set. We explored the characteristics of this subtype classification and investigated genomic alterations, including genomic scar signature scores. First, TNBC was classified into the luminal androgen receptor (LAR) and non-luminal androgen receptor (non-LAR) subtypes based on the AR Allred score (≥ 6 and < 6, respectively). Then, the non-LAR subtype was further classified into the lymphocyte-predominant (LP), lymphocyte-intermediate (LI), and lymphocyte-depleted (LD) groups based on stromal tumor-infiltrating lymphocytes (TILs) (< 20%, > 20% but < 60%, and ≥ 60%, respectively). This classification showed fair agreement with the molecular classification in the test set. The LAR subtype was characterized by a high rate of PIK3CA mutation, CD274 (encodes PD-L1) and PDCD1LG2 (encodes PD-L2) deletion, and a low homologous recombination deficiency (HRD) score. The non-LAR LD TIL group was characterized by a high frequency of NOTCH2 and MYC amplification and a high HRD score.

Machine learning deciphers the significance of mitochondrial regulators on the diagnosis and subtype classification in non-alcoholic fatty liver disease

Background

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent liver disease worldwide and lack of research on the diagnostic utility of mitochondrial regulators in NAFLD. Mitochondrial dysfunction plays a pivotal role in the development and progression of NAFLD, especially oxidative stress and acidity β-oxidative overload. Thus, we aimed to identify and validate a panel of mitochondrial gene expression biomarkers for detection of NAFLD.

Methods

We selected the GSE89632 dataset and identified key mitochondrial regulators by intersecting DEGs, WGCNA modules, and MRGs. Classification of NAFLD subtypes based on these key mitochondrial regulatory factors was performed, and the pattern of immune system infiltration in different NAFLD subtypes were also investigated. RF, LASSO, and SVM-RFE were employed to identify possible diagnostic biomarkers from key mitochondrial regulatory factors and the predictive power was demonstrated through ROC curves. Finally, we validated these potential diagnostic biomarkers in human peripheral blood samples and a high-fat diet-induced NAFLD mouse model.

Results

We identified 25 key regulators of mitochondria and two NAFLD subtypes with different immune infiltration patterns. Four potential diagnostic biomarkers (BCL2L11, NAGS, HDHD3, and RMND1) were screened by three machine learning methods thereby establishing the diagnostic model, which showed favorable predictive power and achieved significant clinical benefit at certain threshold probabilities. Then, through internal and external validation, we identified and confirmed that BCL2L11 was significantly downregulated in NAFLD, while the other three were significantly upregulated.

Conclusion

The four MRGs, namely BCL2L11, NAGS, HDHD3, and RMND1, are novel potential biomarkers for diagnosing NAFLD. A diagnostic model constructed using the four MRGs may aid early diagnosis of NAFLD in clinics.

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

BACKGROUND

Recently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the "Classical" and "Basal-like" subtypes, with the former showing better clinical outcomes than the latter. However, the "molecular" classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.

METHODS

We utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays. We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.

RESULTS

PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling. RNA-seq analysis revealed that the "morphological" subtype (GL vs. DP) corresponded to the "molecular" subtype ("Classical" vs. "Basal-like"). The "morphological" classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.

CONCLUSIONS

PDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.

m6A methylation modification and immune infiltration analysis in osteonecrosis of the femoral head

Osteonecrosis of the femoral head (ONFH) is a elaborate hip disease characterized by collapse of femoral head and osteoarthritis. RNA N6-methyladenosine (m6A) plays a crucial role in a lot of biological processes within eukaryotic cells. However, the role of m6A in the regulation of ONFH remains unclear. In this study, we identified the m6A regulators in ONFH and performed subtype classification. We identified 7 significantly differentially expressed m6A regulators through the analysis of differences between ONFH and normal samples in the Gene Expression Omnibus (GEO) database. A random forest algorithm was employed to monitor these regulators to assess the risk of developing ONFH. We constructed a nomogram based on these 7 regulators. The decision curve analysis suggested that patients can benefit from the nomogram model. We classified the ONFH samples into two m6A models according to these 7 regulators through consensus clustering algorithm. After that, we evaluated those two m6A patterns using principal component analysis. We assessed the scores of those two m6A patterns and their relationship with immune infiltration. We observed a higher m6A score of type A than that of type B. Finally, we performed a cross-validation of crucial m6A regulatory factors in ONFH using external datasets and femoral head bone samples. In conclusion, we believed that the m6A pattern could provide a novel diagnostic strategy and offer new insights for molecularly targeted therapy of ONFH.

[Identification of breast cancer subtypes based on graph convolutional network]

Identification of molecular subtypes of malignant tumors plays a vital role in individualized diagnosis, personalized treatment, and prognosis prediction of cancer patients. The continuous improvement of comprehensive tumor genomics database and the ongoing breakthroughs in deep learning technology have driven further advancements in computer-aided tumor classification. Although the existing classification methods based on gene expression omnibus database take the complexity of cancer molecular classification into account, they ignore the internal correlation and synergism of genes. To solve this problem, we propose a multi-layer graph convolutional network model for breast cancer subtype classification combined with hierarchical attention network. This model constructs the graph embedding datasets of patients' genes, and develops a new end-to-end multi-classification model, which can effectively recognize molecular subtypes of breast cancer. A large number of test data prove the good performance of this new model in the classification of breast cancer subtypes. Compared to the original graph convolutional neural networks and two mainstream graph neural network classification algorithms, the new model has remarkable advantages. The accuracy, weight-F1-score, weight-recall, and weight-precision of our model in seven-category classification has reached 0.851 7, 0.823 5, 0.851 7 and 0.793 6 respectively. In the four-category classification, the results are 0.928 5, 0.894 9, 0.928 5 and 0.865 0 respectively. In addition, compared with the latest breast cancer subtype classification algorithms, the method proposed in this paper also achieved the highest classification accuracy. In summary, the model proposed in this paper may serve as an auxiliary diagnostic technology, providing a reliable option for precise classification of breast cancer subtypes in the future and laying the theoretical foundation for computer-aided tumor classification.

Dry and wet experiments reveal diagnostic clustering and immune landscapes of cuproptosis patterns in patients with ankylosing spondylitis

Cuproptosis is a new manner of mitochondrial cell death induced by copper. There is evidence that serum copper has a crucial impact on ankylosing spondylitis (AS) by copper-induced inflammatory response. However, the molecular mechanisms of cuproptosis modulators in AS remain unknown. We aimed to use a bioinformatics-based method to comprehensively investigate cuproptosis-related subtype identification and immune microenvironment infiltration of AS. Additionally, we further verified the results by in vitro experiments, in which peripheral blood and fibroblast cells from AS patients were used to evaluate the functions of significant cuproptosis modulators on AS. Finally, eight significant cuproptosis modulators were identified by analysis of differences between controls and AS cases from GSE73754 dataset. Eight prognostic cuproptosis modulators (LIPT1, DLD, PDHA1, PDHB, SLC31A1, ATP7A, MTF1, CDKN2A) were identified using a random forest model for prediction of AS risk. A nomogram model of the 8 prognostic cuproptosis modulators was then constructed; the model could be beneficial in clinical settings, as indicated by decision curve analysis. Consensus clustering analysis was used to divide AS patients into two cuproptosis subtypes (clusterA & B) according to significant cuproptosis modulators. The cuproptosis score of each sample was calculated by principal component analysis to quantify cuproptosis subtypes. The cuproptosis scores were higher in clusterB than in clusterA. Additionally, cases in clusterA were closely associated with the immunity of activated B cells, Activated CD4 T cell, Type17 T helper cell and Type2 T helper cell, while cases in clusterB were linked to Mast cell, Neutrophil, Plasmacytoid dendritic cell immunity, indicating that clusterB may be more correlated with AS. Notably, key cuproptosis genes including ATP7A, MTF1, SLC31A1 detected by RT-qPCR with peripheral blood exhibited significantly higher expression levels in AS cases than controls; LIPT1 showed the opposite results; High MTF1 expression is correlated with increased osteogenic capacity. In general, this study of cuproptosis patterns may provide promising biomarkers and immunotherapeutic strategies for future AS treatment.

Diagnostic performance of the adrenal vein to inferior vena cava aldosterone ratio in classifying the subtype of primary aldosteronism

Adrenal vein sampling (AVS) is the standard procedure for distinguishing unilateral primary aldosteronism (PA) from bilateral PA. In cases where only one adrenal vein is successfully cannulated, it has been suggested that subtype classification can be determined based on the ratio of the concentration of aldosterone between the adrenal vein and the inferior vena cava (AV/IVC index). However, diagnostic performance of the ipsilateral versus contralateral AV/IVC index in predicting lateralization has not been directly compared. In a retrospective cohort of 133 patients with confirmed PA who underwent successful AVS, the performance of the AV/IVC index to predict laterality was evaluated and the area under the receiver operating characteristic (AUROC) curves was calculated. In detecting left unilateral PA (n = 47), the AUROC of the right AV/IVC index (RAV/IVC) was significantly higher than the AUROC of the left AV/IVC (LAV/IVC) index (0.967 vs. 0.871, p = 0.008). In detecting right unilateral PA (n = 30), the AUROC of the LAV/IVC index tended to be higher than that of the RAV/IVC index, but the difference did not reach statistical significance (0.966 vs. 0.906, p = 0.08). In detecting left unilateral PA, the sensitivities of the RAV/IVC and LAV/IVC indices were 83% and 46%, respectively, while the specificities of both were above 90%. In detecting right unilateral PA, the sensitivities of the LAV/IVC and RAV/IVC indices were 80% and 43%, respectively, while the specificities of both were above 90%. The AV/IVC index has superior diagnostic performance in detecting contralateral unilateral PA compared to ipsilateral unilateral PA.

Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis

BACKGROUND

RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m¹A/m⁶A/m⁵C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m¹A/m⁶A/m⁵C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood.

METHODS

Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs.

RESULTS

We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A.

CONCLUSIONS

Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m¹A/m⁶A/m⁵C regulators may be associated with RIF.

m6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis

Osteoarthritis (OA) is a common disease in orthopedics. RNA N6-methyladenosine (m6A) exerts an essential effect in a variety of biological processes in the eukaryotes. In this study, we determined the effect of m6A regulators in the OA along with performing the subtype classification. Differential analysis of OA and normal samples in the database of Gene Expression Omnibus identified 9 significantly differentially expressed m6A regulators. These regulators were monitored by a random forest algorithm so as to evaluate the risk of developing OA disease. On the basis of these 9 moderators, a nomogram was established. The results of decision curve analysis suggested that the patients could benefit from a nomogram model. The OA sample was classified as 2 m6A models through a consensus clustering algorithm in accordance with these 9 regulators. These 2 m6A patterns were then assessed with principal component analysis. We also determined the m6A scores for the 2 m6A patterns and their correlation with immune infiltration. The results indicated that type A had a higher m6A score than type B. Thus, we suggest that the m6A pattern may provide a new approach for diagnose and provide novel ideas for molecular targeted therapy of OA.

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics

RATIONALE AND OBJECTIVES

Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance.

MATERIALS AND METHODS

920 patients (mean age 61.2, range, 17-87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively.

RESULTS

The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in ADC, SCC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in ADC, SCC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively.

CONCLUSION

The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.

Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes

BACKGROUND

Epithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.

METHODS

Non-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.

RESULTS

Application of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.

CONCLUSIONS

This robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Identification of key classification features of early cervical squamous cell carcinoma

Despite the tremendous progress in molecular analysis of pan-cancer, little is known regarding molecular classification of cervical squamous cell carcinoma. In this study, we adopted a multi-omics approach to identify potential key classification features of cervical squamous cell carcinoma. Specifically, we analyzed mRNA, and microRNA (miRNA) expression data, as well as DNA methylation and copy number variation in cervical squamous cell carcinoma cases, using datasets obtained from The Cancer Genome Atlas (TCGA). Moreover, we identified molecules in each dimension, as well as integrated and clustered filtered classification features, and used them to distinguish different subtypes. The resulting key classification features were used to establish a classification model for cervical squamous cell carcinoma. Our results revealed two cervical squamous cell carcinoma subtypes, with significant differences across clinical survival levels, as well as 8 key classification features of cervical squamous cell carcinomas. These findings are expected to provide important references for early classification of cervical squamous cell carcinoma and identification of classification markers.

Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning

Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work, a data augmentation method named elastic deformation is proposed to artificially enlarge the image dataset of NSCLC patients with two subtypes (squamous cell carcinoma and large cell carcinoma) of 3158 images. Elastic deformation effectively expanded the dataset by generating new images, in which tumor lesions go through elastic shape transformation. To evaluate the proposed method, two classification models were trained on the original and augmented dataset, respectively. Using augmented dataset for training significantly increased classification metrics including area under the curve (AUC) values of receiver operating characteristics (ROC) curves, accuracy, sensitivity, specificity, and f1-score, thus improved the NSCLC subtype classification performance. These results suggest that elastic deformation could be an effective data augmentation method for NSCLC tumor lesion images, and building classification models with the help of elastic deformation has the potential to serve for clinical lung cancer diagnosis and treatment design.

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method

In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1 min after the injection; phase 3, 5 min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a combination weight was multiplied to the three-phase ROI images and the linearly combined images were fed into a deep learning neural network after concatenation. A deep learning neural network was trained to classify the subtypes of renal cell carcinoma, using the drawn ROIs as inputs and the biopsy results as labels. The network showed about 0.85 accuracy, 0.64–0.98 sensitivity, 0.83–0.93 specificity, and 0.9 AUC. The proposed framework which is based on deep learning method and ROIs provided by radiologists showed promising results in renal cell subtype classification. We hope it will help future research on this subject and it can cooperate with radiologists in classifying the subtype of lesion in real clinical situation.

[Update and Research Progress in the Diagnosis of Primary Aldosteronism]

Primary aldosteronism (PA) is the most common cause of secondary hypertension. The diagnosis procedure of PA includes screening, confirmatory diagnosis and subtype classification. International and national guidelines recommended plasma aldosterone concentration (PAC) to plasma renin activity (PRA) ratio (ARR) to detect possible cases of PA, and one or more tests (fludrocortisone suppression test, saline infusion test, oral sodium loading test, or captopril challenge test) to confirm ARR positive patients. Adrenal venous sampling (AVS) is also recommended as the best method to distinguish unilateral and bilateral adrenal disease when surgical treatment is feasible and desired by the patient. However, many studies find that each of the above diagnostic method has shortcomings. Recently, more and more studies are attempting to explore new methods with higher diagnostic efficiency and more conveniences, including new screening tests, new confirmatory diagnostic tests, new imaging and pathological histology methods. In our studies, the regression model, which included upright PAC, upright PRA, and lowest potassium, is superior to ARR for PA screening; the blood potassium and the ratio of blood potassium to blood sodium after the saline infusion test are not suitable for PA subtyping. This article will review the advances and progress in PA diagnosis.

Most Variable Genes and Transcription Factors in Acute Lymphoblastic Leukemia Patients

Acute lymphoblastic leukemia (ALL) is a hematologic tumor caused by cell cycle aberrations due to accumulating genetic disturbances in the expression of transcription factors (TFs), signaling oncogenes and tumor suppressors. Though survival rate in childhood ALL patients is increased up to 80% with recent medical advances, treatment of adults and childhood relapse cases still remains challenging. Here, we have performed bioinformatics analysis of 207 ALL patients' mRNA expression data retrieved from the ICGC data portal with an objective to mark out the decisive genes and pathways responsible for ALL pathogenesis and aggression. For analysis, 3361 most variable genes, including 276 transcription factors (out of 16,807 genes) were sorted based on the coefficient of variance. Silhouette width analysis classified 207 ALL patients into 6 subtypes and heat map analysis suggests a need of large and multicenter dataset for non-overlapping subtype classification. Overall, 265 GO terms and 32 KEGG pathways were enriched. The lists were dominated by cancer-associated entries and highlight crucial genes and pathways that can be targeted for designing more specific ALL therapeutics. Differential gene expression analysis identified upregulation of two important genes, JCHAIN and CRLF2 in dead patients' cohort suggesting their possible involvement in different clinical outcomes in ALL patients undergoing the same treatment.

Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL.

Methods

Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort (N = 426), GSE10846 (N = 350) cohort and GSE4475 cohort (N = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA.

Results

Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway.

Conclusions

Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0580-4) contains supplementary material, which is available to authorized users.

MM2S: personalized diagnosis of medulloblastoma patients and model systems

Background

Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes (MM2S) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers.

Results

The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes.

Conclusions

Our MM2S package can be used to generate predictions without having to rely on an external web server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/web/packages/MM2S/, as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.

Electronic supplementary material

The online version of this article (doi:10.1186/s13029-016-0053-y) contains supplementary material, which is available to authorized users.

Identification of two phylogenetic lineages of equine hepacivirus and high prevalence in Brazil

Non-primate hepacivirus (NPHV), as described in horses, is the virus most genetically related to hepatitis C virus (HCV). Although detected worldwide, limited data on genomic variability and distribution of NPHV are available in Latin America. The aim of this study was to investigate the genetic diversity and prevalence of equine NPHV in Brazil. Thirteen percent of 202 equines from three Brazilian states were positive for NPHV genome by reverse transcriptase PCR. Nucleotide sequences of the partial NS5B genome presented the greatest diversity described to date (25.6%), which is comparable to the upper limit of diversity for HCV subtype classification for the same region. Phylogenetic analysis revealed that Brazilian NPHV sequences along with isolates worldwide form two strongly supported clades (pp = 1.0) suggesting the existence of two distinct lineages.

Gene-expression profiles correlate with the efficacy of anti-EGFR therapy and chemotherapy for colorectal cancer

BACKGROUND

Comprehensive gene-expression analysis is very useful for classifying specific cancers into subgroups on the basis of their biological characteristics; it is used both prognostically and predictively. The purpose of this study was to classify unresectable advanced or recurrent colorectal cancer (CRC) by gene-expression profiling of formalin-fixed paraffin-embedded tissues and to correlate CRC subgroups with clinicopathological and molecular features and clinical outcomes.

METHODS

One hundred patients with advanced or recurrent CRC were enrolled. RNA extracted from FFPE tissues was subjected to gene-expression microarray analysis.

RESULTS

The patients were stratified into four subgroups (subtypes A1, A2, B1, and B2) by unsupervised hierarchical clustering. By use of principle-components analysis (PCA), the patients were divided into subtypes A and B on the basis of component 1 and into subtypes 1 and 2 on the basis of component 2. Subtype A was significantly enriched among patients without the KRAS mutation and with an earlier clinical stage at diagnosis. With regard to anti-EGFR therapy, progression-free survival (PFS) was better for patients in subtype A without the KRAS mutation than for those with the KRAS mutation (P = 0.047). PFS for patients without the KRAS mutation in subtype B was comparable with that for patients with the KRAS mutation (P = 0.55). Similar results were observed in a validation set.

CONCLUSION

We found that gene-expression profiles enabled stratification of CRC patients into four subgroups. The efficacy of anti-EGFR therapy was correlated with component 1 from PCA. This comprehensive study may explain the heterogeneity of unresectable advanced or recurrent CRC and could be useful for identifying novel biomarkers for CRC treatment.

Saponins in the genus Panax L. (Araliaceae): a systematic review of their chemical diversity

The Panax genus is a crucial source of natural medicines that has benefited human health for a long time. Three valuable medicinal herbs, namely Panax ginseng, Panax quinquefolius, and Panax notoginseng, have received considerable interest due to their extensive application in clinical therapy, healthcare products, and as foods and food additives world-wide. Panax species are known to contain abundant levels of saponins, also dubbed ginsenosides, which refer to a series of dammarane or oleanane type triterpenoid glycosides. These saponins exhibit modulatory effects to the central nervous system and beneficial effects to patients suffering from cardiovascular diseases, and also have anti-diabetic and anti-tumor properties. To the end of 2012, at least 289 saponins were reported from eleven different Panax species. This comprehensive review describes the advances in the phytochemistry of the genus Panax for the period 1963-2012, based on the 134 cited references. The reported saponins can be classified into protopanaxadiol, protopanaxatriol, octillol, oleanolic acid, C17 side-chain varied, and miscellaneous subtypes, according to structural differences in sapogenins. The investigational history of Panax is also reviewed, with special attention being paid to the structural features of the six different subtypes, together with their (1)H and (13)C NMR spectroscopic characteristics which are useful for determining their structures and absolute configuration.