X-box binding protein 1 (XBP1): a potential role in chemotherapy response,clinical pathologic features,non-inflamed tumour microenvironment for breast cancer

X-box binding protein 1 (XBP1) is mainly expressed in breast cancer (BC) in human cancers. Its tumorigenesis and favourable prognosis are contradictory, and its essential role in chemotherapeutic response and immunosuppression is unknown in BC. The study firstly identified XBP1 who received neoadjuvant chemotherapy (NAC) from {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055 and {"type":"entrez-geo","attrs":{"text":"GSE24460","term_id":"24460"}}GSE24460. Associations between XBP1 expression and clinicopathological characteristics was investigated using Oncomine, TCGA, UALCAN and bc-GenExMiner. The prognostic value of XBP1 was assessed using the Kaplan–Meier Plotter, bc-GenExMiner, {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, and {"type":"entrez-geo","attrs":{"text":"GSE25056","term_id":"25056"}}GSE25056. Furthermore, we systematically correlated XBP1 and immunological characteristics in the BC tumour microenvironment (TME) using TISIDB, TIMER, {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, {"type":"entrez-geo","attrs":{"text":"GSE25056","term_id":"25056"}}GSE25056 and TCGA dataset. Finally, an essential role of XBP1 in chemotherapy response was evaluated based on {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, {"type":"entrez-geo","attrs":{"text":"GSE25065","term_id":"25065"}}GSE25065, {"type":"entrez-geo","attrs":{"text":"GSE24460","term_id":"24460"}}GSE24460, {"type":"entrez-geo","attrs":{"text":"GSE5846","term_id":"5846"}}GSE5846, ROC Plotter and CELL databases. Furthermore, XBP1 mRNA expression levels were obviously highest in BC among human cancers and were significantly related to a good prognosis. In addition, XBP1 mRNA and protein levels were higher in the luminal subtype than in normal tissues and basal-like subtype, which might be attributed to membrane transport-related processes. Apart from BC, negative immunological correlations of XBP1 were not observed in other malignancies. XBP1 might shape the non-inflamed TME in BC. Finally, XBP1 expression was higher in chemo-resistive than chemo-sensitive cases, it had a predictive value and could independently predict chemotherapy response in BC patients receiving NAC. Our study suggests that the essential role of XBP1 in clinical pathologic features, non-inflamed TME, chemotherapy response in BC.     

Expression of the Genetic Suppressor Element 24.2 (GSE24.2) Decreases DNA Damage and Oxidative Stress in X-Linked Dyskeratosis Congenita Cells

The predominant X-linked form of Dyskeratosis congenita results from mutations in DKC1, which encodes dyskerin, a protein required for ribosomal RNA modification that is also a component of the telomerase complex. We have previously found that expression of an internal fragment of dyskerin ({"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2) rescues telomerase activity in X-linked dyskeratosis congenita (X-DC) patient cells. Here we have found that an increased basal and induced DNA damage response occurred in X-DC cells in comparison with normal cells. DNA damage that is also localized in telomeres results in increased heterochromatin formation and senescence. Expression of a cDNA coding for {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 rescues both global and telomeric DNA damage. Furthermore, transfection of bacterial purified or a chemically synthesized {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 peptide is able to rescue basal DNA damage in X-DC cells. We have also observed an increase in oxidative stress in X-DC cells and expression of {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 was able to diminish it. Altogether our data indicated that supplying {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2, either from a cDNA vector or as a peptide reduces the pathogenic effects of Dkc1 mutations and suggests a novel therapeutic approach.     

Exploring the Novel Computational Drug Target and Associated Key Pathways of Oral Cancer

Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. {"type":"entrez-geo","attrs":{"text":"GSE74530","term_id":"74530"}}GSE74530, {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558, and {"type":"entrez-geo","attrs":{"text":"GSE3524","term_id":"3524"}}GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to calculate the upregulated and downregulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein–protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for {"type":"entrez-geo","attrs":{"text":"GSE74530","term_id":"74530"}}GSE74530, {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558, and {"type":"entrez-geo","attrs":{"text":"GSE3524","term_id":"3524"}}GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.     

Circulating lncRNA UCA1 and lncRNA PGM5-AS1 act as potential diagnostic biomarkers for early-stage colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common and significant malignant diseases worldwide. In the present study, we evaluated two long non-coding RNAs (lncRNAs) in CRC patients as diagnostic markers for early-stage CRC.Methods: Using Gene Expression Omnibus (GEO) datasets {"type":"entrez-geo","attrs":{"text":"GSE102340","term_id":"102340"}}GSE102340, {"type":"entrez-geo","attrs":{"text":"GSE126092","term_id":"126092"}}GSE126092, {"type":"entrez-geo","attrs":{"text":"GSE109454","term_id":"109454"}}GSE109454 and {"type":"entrez-geo","attrs":{"text":"GSE115856","term_id":"115856"}}GSE115856, 14 differentially expressed lncRNAs were identified between cancer and adjacent tissues, among which, the two most differentially expressed were confirmed using quantitative real-time polymerase chain reaction (qRT-PCR) in 200 healthy controls and 188 CRC patients. A receiver operating characteristic (ROC) analysis was employed to evaluate the diagnostic accuracy for CRC.Results: From four GEO datasets, three up-regulated and eleven down-regulated lncRNAs were identified in CRC tissues, among which, lncRNA urothelial carcinoma-associated 1 (UCA1) and lncRNA phosphoglucomutase 5-antisense RNA 1 (PGM5-AS1) were the most significantly up- and down-regulated lncRNAs in CRC patient plasma, respectively. The area under the ROC curve was calculated to be 0.766, 0.754 and 0.798 for UCA1, PGM5-AS1 and the combination of these two lncRNAs, respectively. Moreover, the diagnostic potential of these two lncRNAs was even higher for the early stages of CRC. The combination of UCA1 and PGM5-AS1 enhanced the AUC to 0.832, and when the lncRNAs were used with carcinoembryonic antigen (CEA), the AUC was further improved to 0.874.Conclusion: In the present study, we identified two lncRNAs, UCA1 and PGM5-AS1, in CRC patients’ plasma, which have the potential to be used as diagnostic biomarkers of CRC.     

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the {"type":"entrez-geo","attrs":{"text":"GSE28405","term_id":"28405"}}GSE28405, {"type":"entrez-geo","attrs":{"text":"GSE38246","term_id":"38246"}}GSE38246, and {"type":"entrez-geo","attrs":{"text":"GSE51808","term_id":"51808"}}GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.     

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma

Autophagy is involved in cancer initiation and progression but its role in uveal melanoma (UM) was rarely investigated. Herein, we built an autophagy-related gene (ARG) risk model of UM patients by univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression model and filtrated out nine prognostic ARGs in The Cancer Genome Atlas (TCGA) cohort. Survival and Receiver Operating Characteristic (ROC) Curve analysis in the TCGA and other four independent UM cohorts ({"type":"entrez-geo","attrs":{"text":"GSE22138","term_id":"22138"}}GSE22138, {"type":"entrez-geo","attrs":{"text":"GSE27831","term_id":"27831"}}GSE27831, {"type":"entrez-geo","attrs":{"text":"GSE44295","term_id":"44295"}}GSE44295 and {"type":"entrez-geo","attrs":{"text":"GSE84976","term_id":"84976"}}GSE84976) proved that the ARG-signature possessed robust and steady prognosis predictive ability. We calculated risk scores for patients included in our study and patients with higher risk scores showed worse clinical outcomes. We found the expressions of the nine ARGs were significantly associated with clinical and molecular features (including risk score) and overall survival (OS) of UM patients. Furthermore, we utilized univariate and multivariate Cox regression analyses to determine the independent prognostic ability of the ARG-signature. Functional enrichment analysis showed the ARG-signature was correlated with several immune-related processes and pathways like T-cell activation and T-cell receptor signaling pathway. Gene set enrichment analysis (GSEA) found tumor hallmarks including angiogenesis, IL6-JAK-STAT3-signaling, reactive oxygen species pathway and oxidative phosphorylation were enriched in high-risk UM patients. Finally, infiltrations of several immune cells and immune-related scores were found significantly associated with the ARG-signature. In conclusion, the ARG-signature might be a strong predictor for evaluating the prognosis and immune infiltration of UM patients.     

Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis,Osteoarthritis, and Periodontal Disease: An Omics-Based Study

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs {"type":"entrez-geo","attrs":{"text":"GSE97779","term_id":"97779"}}GSE97779, {"type":"entrez-geo","attrs":{"text":"GSE123492","term_id":"123492"}}GSE123492, and {"type":"entrez-geo","attrs":{"text":"GSE24897","term_id":"24897"}}GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene–gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that “apoptotic regulation” and “inflammasomes” were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.     

Role of PCK2 in the proliferation of vascular smooth muscle cells in neointimal hyperplasia

Vascular smooth muscle cell (VSMC) proliferation is a hallmark of neointimal hyperplasia (NIH) in atherosclerosis and restenosis post-balloon angioplasty and stent insertion. Although numerous cytotoxic and cytostatic therapeutics have been developed to reduce NIH, it is improbable that a multifactorial disease can be successfully treated by focusing on a preconceived hypothesis. We, therefore, aimed to identify key molecules involved in NIH via a hypothesis-free approach. We analyzed four datasets ({"type":"entrez-geo","attrs":{"text":"GSE28829","term_id":"28829"}}GSE28829, {"type":"entrez-geo","attrs":{"text":"GSE43292","term_id":"43292"}}GSE43292, {"type":"entrez-geo","attrs":{"text":"GSE100927","term_id":"100927"}}GSE100927, and {"type":"entrez-geo","attrs":{"text":"GSE120521","term_id":"120521"}}GSE120521), evaluated differentially expressed genes (DEGs) in wire-injured femoral arteries of mice, and determined their association with VSMC proliferation in vitro. Moreover, we performed RNA sequencing on platelet-derived growth factor (PDGF)-stimulated human VSMCs (hVSMCs) post-phosphoenolpyruvate carboxykinase 2 (PCK2) knockdown and investigated pathways associated with PCK2. Finally, we assessed NIH formation in Pck2 knockout (KO) mice by wire injury and identified PCK2 expression in human femoral artery atheroma. Among six DEGs, only PCK2 and RGS1 showed identical expression patterns between wire-injured femoral arteries of mice and gene expression datasets. PDGF-induced VSMC proliferation was attenuated when hVSMCs were transfected with PCK2 siRNA. RNA sequencing of PCK2 siRNA-treated hVSMCs revealed the involvement of the Akt-FoxO-PCK2 pathway in VSMC proliferation via Akt2, Akt3, FoxO1, and FoxO3. Additionally, NIH was attenuated in the wire-injured femoral artery of Pck2-KO mice and PCK2 was expressed in human femoral atheroma. PCK2 regulates VSMC proliferation in response to vascular injury via the Akt-FoxO-PCK2 pathway. Targeting PCK2, a downstream signaling mediator of VSMC proliferation, may be a novel therapeutic approach to modulate VSMC proliferation in atherosclerosis.     

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, {"type":"entrez-geo","attrs":{"text":"GSE1993","term_id":"1993"}}GSE1993 and {"type":"entrez-geo","attrs":{"text":"GSE4422","term_id":"4422"}}GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.     

TGF-β-induced PLEK2 promotes metastasis and chemoresistance in oesophageal squamous cell carcinoma by regulating LCN2

Oesophageal squamous cell carcinoma (ESCC) has a relatively unfavourable prognosis due to metastasis and chemoresistance. Our previous research established a comprehensive ESCC database ({"type":"entrez-geo","attrs":{"text":"GSE53625","term_id":"53625"}}GSE53625). After analysing data from TCGA database and {"type":"entrez-geo","attrs":{"text":"GSE53625","term_id":"53625"}}GSE53625, we found that PLEK2 predicted poor prognosis in ESCC. Moreover, PLEK2 expression was also related to the overall survival of ESCC patients undergoing chemotherapy. Repression of PLEK2 decreased the proliferation, migration, invasion and chemoresistance of ESCC cells in vitro and decreased tumorigenicity and distant metastasis in vivo. Mechanistically, luciferase reporter assay and chromatin immunoprecipitation assay suggested that TGF-β stimulated the process that Smad2/3 binds to the promoter sequences of PLEK2 and induced its expression. RNA-seq suggested LCN2 might a key molecular regulated by PLEK2. LCN2 overexpression in PLEK2 knockdown ESCC cells reversed the effects of decreased migration and invasion. In addition, TGF-β induced the expression of LCN2, but the effect disappeared when PLEK2 was knockdown. Moreover, AKT was phosphorylated in all regulatory processes. This study detected the major role of PLEK2 in driving metastasis and chemoresistance in ESCC by regulating LCN2, which indicates the potential use of PLEK2 as a biomarker to predict prognosis and as a therapeutic target for ESCC.Subject terms: Cancer, Molecular biology     

Structural Variation-Associated Expression Changes Are Paralleled by Chromatin Architecture Modifications

Copy number variants (CNVs) influence the expression of genes that map not only within the rearrangement, but also to its flanks. To assess the possible mechanism(s) underlying this “neighboring effect”, we compared intrachromosomal interactions and histone modifications in cell lines of patients affected by genomic disorders and control individuals. Using chromosome conformation capture (4C-seq), we observed that a set of genes flanking the Williams-Beuren Syndrome critical region (WBSCR) were often looping together. The newly identified interacting genes include AUTS2, mutations of which are associated with autism and intellectual disabilities. Deletion of the WBSCR disrupts the expression of this group of flanking genes, as well as long-range interactions between them and the rearranged interval. We also pinpointed concomitant changes in histone modifications between samples.We conclude that large genomic rearrangements can lead to chromatin conformation changes that extend far away from the structural variant, thereby possibly modulating expression globally and modifying the phenotype.GEO Series accession number: {"type":"entrez-geo","attrs":{"text":"GSE33784","term_id":"33784"}}GSE33784, {"type":"entrez-geo","attrs":{"text":"GSE33867","term_id":"33867"}}GSE33867.     

SLC25A38 as a novel biomarker for metastasis and clinical outcome in uveal melanoma

Risk of metastasis is increased by the presence of chromosome 3 monosomy in uveal melanoma (UM). This study aimed to identify more accurate biomarker for risk of metastasis in UM. A total of 80 patients with UM from TCGA were assigned to two groups based on the metastatic status, and bioinformatic analyses were performed to search for critical genes for risk of metastasis. SLC25A38, located on chromosome 3, was the dominant downregulated gene in metastatic UM patients. Low expression of SLC25A38 was an independent predictive and prognostic factor in UM. The predictive potential of SLC25A38 expression was superior to that of pervious reported biomarkers in both TCGA cohort and {"type":"entrez-geo","attrs":{"text":"GSE22138","term_id":"22138"}}GSE22138 cohort. Subsequently, its role in promoting metastasis was explored in vitro and in vivo. Knock-out of SLC25A38 could enhance the migration ability of UM cells, and promote distant metastasis in mice models. Through the inhibition of CBP/HIF-mediated pathway followed by the suppression of pro-angiogenic factors, SLC25A38 was situated upstream of metastasis-related pathways, especially angiogenesis. Low expression of SLC25A38 promotes angiogenesis and metastasis, and identifies increased metastatic risk and worse survival in UM patients. This finding may further improve the accuracy of prognostic prediction for UM.Subject terms: Eye cancer, Prognostic markers     

Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director''s Challenge Consortium. Four independent validation datasets, including {"type":"entrez-geo","attrs":{"text":"GSE5843","term_id":"5843"}}GSE5843, {"type":"entrez-geo","attrs":{"text":"GSE8894","term_id":"8894"}}GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.     

Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability

Background

Clinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence.

Methodology/Principal Findings

Studies were performed on normal mucosa, adenoma, and carcinoma samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS). The data was evaluated using pair-wise comparisons and data decomposition into singular value decomposition (SVD) modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Expressional profiles obtained in 105 samples of whole tissue sections were used to establish oncogenic signaling alterations in progression of CRC, while those representing 40 microdissected specimens were used to select differences in KEGG pathways between epithelium and mucosa. Based on a consensus of the results obtained by two normalization algorithms, and two probe set sorting criteria, we identified 14 and 17 KEGG signaling and metabolic pathways that are significantly altered between normal and tumor samples and between benign and malignant tumors, respectively. Several of them were also selected from the raw microarray data of 2 recently published studies ({"type":"entrez-geo","attrs":{"text":"GSE4183","term_id":"4183"}}GSE4183 and {"type":"entrez-geo","attrs":{"text":"GSE8671","term_id":"8671"}}GSE8671).

Conclusion/Significance

Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results.     

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): An Algorithm for Joint Optimization of Discrimination and Calibration

Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., {"type":"entrez-geo","attrs":{"text":"GSE2034","term_id":"2034"}}GSE2034, {"type":"entrez-geo","attrs":{"text":"GSE2990","term_id":"2990"}}GSE2990, and Chanrion''s datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine ( = 0.03,  = 0.13, and <0.001) and Logistic Regression ( = 0.006,  = 0.008, and <0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine ( = 0.38,  = 0.29, and  = 0.047) and Logistic Regression ( = 0.38,  = 0.04, and <0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making.