The PAM50 classifier is widely used for breast tumor intrinsic subtyping based on gene expression. Clinical subtyping, however, is based on immunohistochemistry assays of 3-4 biomarkers. Subtype ...calls by these two methods do not completely match even on comparable subtypes. Nevertheless, the estrogen receptor (ER)-balanced subset for gene-centering in PAM50 subtyping, is selected based on clinical ER status. Here we present a new method called Principle Component Analysis-based iterative PAM50 subtyping (PCA-PAM50) to perform intrinsic subtyping in ER status unbalanced cohorts. This method leverages PCA and iterative PAM50 calls to derive the gene expression-based ER status and a subsequent ER-balanced subset for gene centering. Applying PCA-PAM50 to three different breast cancer study cohorts, we observed improved consistency (by 6-9.3%) between intrinsic and clinical subtyping for all three cohorts. Particularly, a more aggressive subset of luminal A (LA) tumors as evidenced by higher MKI67 gene expression and worse patient survival outcomes, were reclassified as luminal B (LB) increasing the LB subtype consistency with IHC by 25-49%. In conclusion, we show that PCA-PAM50 enhances the consistency of breast cancer intrinsic and clinical subtyping by reclassifying an aggressive subset of LA tumors into LB. PCA-PAM50 code is available at ftp://ftp.wriwindber.org/ .
Purpose
To explore the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer.
Methods
The clinical and RNA-Seq data for ...951 (75 positive and 876 negative margins) primary breast cancer patients from The Cancer Genome Atlas (TCGA) were used. The role of each clinicopathologic factor for margin prediction and also their impact on survival were evaluated using logistic regression, Fisher’s exact test, and Cox proportional hazards regression models. In addition, differential expression analysis on a matched dataset (71 positive and 71 negative margins) was performed using Deseq2 and LASSO regression.
Results
Association studies showed that higher stage, larger tumor size (T), positive lymph nodes (N), and presence of distant metastasis (M) significantly contributed (
p
≤ 0.05) to positive surgical margins. In case of surgery, lumpectomy was significantly associated with positive margin compared to mastectomy. Moreover, PAM50 Luminal A subtype had higher chance of positive margin resection compared to Basal-like subtype. Survival models demonstrated that positive margin status along with higher stage, higher TNM, and negative hormone receptor status was significant for disease progression. We also found that margin status might be a surrogate of tumor stage. In addition, 29 genes that could be potential positive margin predictors and 8 pathways were identified from molecular data analysis.
Conclusion
The occurrence of positive margins after surgery was associated with various clinical factors, similar to the findings reported in earlier studies. In addition, we found that the PAM50 intrinsic subtype Luminal A has more chance of obtaining positive margins compared to Basal type. As the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes was also identified for potential prediction of the margin status which needs to be validated using a larger sample set.
Purpose
Molecular similarities have been reported between basal-like breast cancer (BLBC) and high-grade serous ovarian cancer (HGSOC). To date, there have been no prognostic biomarkers that can ...provide risk stratification and inform treatment decisions for both BLBC and HGSOC. In this study, we developed a molecular signature for risk stratification in BLBC and further validated this signature in HGSOC.
Methods
RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) project for 190 BLBC and 314 HGSOC patients. Analyses of differentially expressed genes between recurrent vs. non-recurrent cases were performed using different bioinformatics methods. Gene Signature was established using weighted linear combination of gene expression levels. Their prognostic performance was evaluated using survival analysis based on progression-free interval (PFI) and disease-free interval (DFI).
Results
63 genes were differentially expressed between 18 recurrent and 40 non-recurrent BLBC patients by two different methods. The recurrence index (RI) calculated from this 63-gene signature significantly stratified BLBC patients into two risk groups with 38 and 152 patients in the low-risk (RI-Low) and high-risk (RI-High) groups, respectively (
p
= 0.0004 and 0.0023 for PFI and DFI, respectively). Similar performance was obtained in the HGSOC cohort (
p
= 0.0131 and 0.004 for PFI and DFI, respectively). Multivariate Cox regression adjusting for age, grade, and stage showed that the 63-gene signature remained statistically significant in stratifying HGSOC patients (
p
= 0.0005).
Conclusion
A gene signature was identified to predict recurrence in BLBC and HGSOC patients. With further validation, this signature may provide an additional prognostic tool for clinicians to better manage BLBC, many of which are triple-negative and HGSOC patients who are currently difficult to treat.
•The biodiesel production was investigated using marine microalgae Nannochloropsis salina.•CaO nanocatalyst obtained from egg shell delivers high catalytic performance.•The biodiesel production was ...optimized by ANN model.•86.1% of biodiesel yield was achieved under optimized conditions.•The GCMS analysis was confirmed the ester derivatives found in produced biodiesel.
In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characterized by SEM and FTIR analysis. Process variables optimization for biodiesel production was studied using RSM and ANN. The R2 values for RSM and ANN was found to be 0.8751 and 0.957 which showed that the model was significantly fit with the experimental data. The maximum FAME conversion for the synthesized nanocatalyst CaO was found to be 86.1% under optimum process conditions (nanocatalyst amount: 3% (w/v); oil to methanol ratio 1:6 (v/v); reaction temperature: 60 °C; reaction time 55 min). Concentration of FAME present in biodiesel was identified by GC–MS analysis.
Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for ...patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors.
We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers.
We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors.
This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.
One of the important modes of pre-mRNA post-transcriptional modification is alternative splicing. Alternative splicing allows creation of many distinct mature mRNA transcripts from a single gene by ...utilizing different splice sites. In plants like Arabidopsis thaliana, the most common type of alternative splicing is intron retention. Many studies in the past focus on positional distribution of retained introns (RIs) among different genic regions and their expression regulations, while little systematic classification of RIs from constitutively spliced introns (CSIs) has been conducted using machine learning approaches. We used random forest and support vector machine (SVM) with radial basis kernel function (RBF) to differentiate these two types of introns in Arabidopsis. By comparing coordinates of introns of all annotated mRNAs from TAIR10, we obtained our high-quality experimental data. To distinguish RIs from CSIs, We investigated the unique characteristics of RIs in comparison with CSIs and finally extracted 37 quantitative features: local and global nucleotide sequence features of introns, frequent motifs, the signal strength of splice sites, and the similarity between sequences of introns and their flanking regions. We demonstrated that our proposed feature extraction approach was more accurate in effectively classifying RIs from CSIs in comparison with other four approaches. The optimal penalty parameter C and the RBF kernel parameter Formula: see text in SVM were set based on particle swarm optimization algorithm (PSOSVM). Our classification performance showed F-Measure of 80.8% (random forest) and 77.4% (PSOSVM). Not only the basic sequence features and positional distribution characteristics of RIs were obtained, but also putative regulatory motifs in intron splicing were predicted based on our feature extraction approach. Clearly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.
•Microalgae was pretreated using physical and chemical methods.•Biodiesel was produced using algal oil extracted from Nannochloropsis oculata.•PEG encapsulated Mn doped ZnO nanocatalyst was ...synthesized and utilized for biodiesel production.•The maximum biodiesel yield of 87.5% was obtained RSM method.
In this present work nanocomposite composed of Mn-ZnO capped with Poly Ethylene Glycol (PEG) was utilized as heterogeneous catalyst for the transesterification of oil extracted from Nannochloropsis oculata into biodiesel using methanol as an acyl acceptor. The synthesized Mn-ZnO novel nanocomposite capped with Poly Ethylene Glycol (PEG) was characterized by using SEM and XRD. Lipid contents from the microalgae were extracted by sonication and biphasic solvent method. The process parameters involved for heterogeneous catalysis of N. oculata to biodiesel were optimized and found to be oil to methanol molar ratio of 1:15 (mol:mol), catalyst loading 3.5% (w/w) and reaction temperature of 60 °C for 4 h of reaction time by Response Surface Method. The reusability studies showed that the nano-catalyst can be reused efficiently for 4 cycles. The yield of biodiesel obtained from N. oculata species using Mn-ZnO nanocomposite capped with PEG was 87.5%.
Alternatively spliced introns are the ones that are usually spliced but can be occasionally retained in a transcript isoform. They are the most frequently used alternative splice form in plants (~50% ...of alternative splicing events).
Chlamydomonas reinhardtii
, a unicellular alga, is a good model to understand alternative splicing (AS) in plants from an evolutionary perspective as it diverged from land plants a billion years ago. Using over 7 million cDNA sequences from both pyrosequencing and Sanger sequencing, we found that a much higher percentage of genes (~20% of multi-exon genes) undergo AS than previously reported (3–5%). We found a full component of SR and SR-like proteins possibly involved in AS. The most prevalent type of AS event (40%) was retention of introns, most of which were supported by multiple cDNA evidence (72%) while only 20% of them have coding capacity. By comparing retained and constitutive introns, we identified sequence features potentially responsible for the retention of introns, in the framework of an “intron definition” model for splicing. We find that retained introns tend to have a weaker 5′ splice site, more Gs in their poly-pyrimidine tract and a lesser conservation of nucleotide ‘C’ at position −3 of the 3′ splice site. In addition, the sequence motifs found in the potential branch-point region differed between retained and constitutive introns. Furthermore, the enrichment of G-triplets and C-triplets among the first and last 50 nt of the introns significantly differ between constitutive and retained introns. These could serve as intronic splicing enhancers. All the alternative splice forms can be accessed at
http://bioinfolab.miamioh.edu/cgi-bin/PASA_r20140417/cgi-bin/status_report.cgi?db=Chre_AS
.
The ocular lens contains only two cell types: epithelial cells and fiber cells. The epithelial cells lining the anterior hemisphere have the capacity to continuously proliferate and differentiate ...into lens fiber cells that make up the large proportion of the lens mass. To understand the transcriptional changes that take place during the differentiation process, high-throughput RNA-Seq of newborn mouse lens epithelial cells and lens fiber cells was conducted to comprehensively compare the transcriptomes of these two cell types.
RNA from three biologic replicate samples of epithelial and fiber cells from newborn FVB/N mouse lenses was isolated and sequenced to yield more than 24 million reads per sample. Sequence reads that passed quality filtering were mapped to the reference genome using Genomic Short-read Nucleotide Alignment Program (GSNAP). Transcript abundance and differential gene expression were estimated using the Cufflinks and DESeq packages, respectively. Gene Ontology enrichment was analyzed using GOseq. RNA-Seq results were compared with previously published microarray data. The differential expression of several biologically important genes was confirmed using reverse transcription (RT)-quantitative PCR (qPCR).
Here, we present the first application of RNA-Seq to understand the transcriptional changes underlying the differentiation of epithelial cells into fiber cells in the newborn mouse lens. In total, 6,022 protein-coding genes exhibited differential expression between lens epithelial cells and lens fiber cells. To our knowledge, this is the first study identifying the expression of 254 long intergenic non-coding RNAs (lincRNAs) in the lens, of which 86 lincRNAs displayed differential expression between the two cell types. We found that RNA-Seq identified more differentially expressed genes and correlated with RT-qPCR quantification better than previously published microarray data. Gene Ontology analysis showed that genes upregulated in the epithelial cells were enriched for extracellular matrix production, cell division, migration, protein kinase activity, growth factor binding, and calcium ion binding. Genes upregulated in the fiber cells were enriched for proteosome complexes, unfolded protein responses, phosphatase activity, and ubiquitin binding. Differentially expressed genes involved in several important signaling pathways, lens structural components, organelle loss, and denucleation were also highlighted to provide insights into lens development and lens fiber differentiation.
RNA-Seq analysis provided a comprehensive view of the relative abundance and differential expression of protein-coding and non-coding transcripts from lens epithelial cells and lens fiber cells. This information provides a valuable resource for studying lens development, nuclear degradation, and organelle loss during fiber differentiation, and associated diseases.
Summary
The availability of the smart existing systems utilizes the system assets for crucial internet of things (IoT). The IoT visions to develop effectively with the processing model. Past ...customary versatile figuring set‐ups utilize advanced mobile phones and portables. A sensor could drop out for various reasons, for the system to act naturally adjusting and consider multipath steering. Dynamic topology for a remote sensor arranges all the detecting hubs that are associated together for gathering information that are transmitted. The goals that are accompanying are set for exploring the work that are to be finished. Several sensor hubs in the system are arranged for the improvement of proficiency dynamic topology. The novel strategy has been proposed for the improvement in oneself sorting out, multi‐bounce systems for the remote sensor hubs. Because of its ability to decrease energy utilisation, a cluster‐based model is the best in the remote sensor arrangement. The cluster head selection processes are a lumbering procedure that influences execution on the system execution. Few investigations propose the determination of the cluster head techniques that are being large portion of them that are not fitting for a powerful grouping condition. The calculations for the proposed methodology work better inside a solitary bounce grouping model structure, and the system lifetime establishes a major issue if there should be an occurrence of multi‐jump bunching conditions. The manuscript presents cluster head determination technique novel single‐hop and multi‐hop cluster head selection by GA (S/M HCH‐GA) in wireless sensor networks (WSNs) for both one‐hop as well as multi‐hop cluster models. The technique that is proposed intends to meet the prerequisites of dynamic situations; this is done by choosing cluster head that is dependent on the energy that remains along with the energy that is consumed.
Dynamic topology for a remote sensor arranges all the detecting hubs that are associated together for gathering information that are transmitted. The goals that are accompanying are set for exploring the work that are to be finished. The novel strategy has been proposed for the improvement in oneself sorting out, multi‐bounce systems for the remote sensor hubs. The manuscript presents cluster head determination technique novel single‐hop and multi‐hop cluster head selection by GA (S/M HCH‐GA) in WSNs for both one‐hop as well as multi‐hop cluster models. The technique that is proposed intends to meet the prerequisites of dynamic situations; this is done by choosing cluster head that is dependent on the energy that remains along with the energy that is consumed.
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