Two-gene classifiers have attracted a broad interest for their simplicity and practicality. Most existing two-gene classification algorithms were involved in exhaustive search that led to their low time-efficiencies. In this study, we proposed two new two-gene classification algorithms which used simple univariate gene selection strategy and constructed simple classification rules based on optimal cut-points for two genes selected. We detected the optimal cut-point with the information entropy principle. We applied the two-gene classification models to eleven cancer gene expression datasets and compared their classification performance to that of some established two-gene classification models like the top-scoring pairs model and the greedy pairs model, as well as standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. These comparisons indicated that the performance of our two-gene classifiers was comparable to or better than that of compared models.
It has been known that three core transcription factors (TFs), NANOG, OCT4, and SOX2, collaborate to form a transcriptional circuitry to regulate pluripotency and self-renewal of human embryonic stem (ES) cells. Similarly, MYC also plays an important role in regulating pluripotency and self-renewal of human ES cells. However, the precise mechanism by which the transcriptional regulatory networks control the activity of ES cells remains unclear. In this study, we reanalyzed an extended core network, which includes the set of genes that are cobound by the three core TFs and additional TFs that also bind to these cobound genes. Our results show that beyond the core transcriptional network, additional transcriptional networks are potentially important in the regulation of the fate of human ES cells. Several gene families that encode TFs play a key role in the transcriptional circuitry of ES cells. We also demonstrate that MYC acts independently of the core module in the regulation of the fate of human ES cells, consistent with the established argument. We find that TP53 is a key connecting molecule between the core-centered and MYC-centered modules. This study provides additional insights into the underlying regulatory mechanisms involved in the fate determination of human ES cells.
One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.
The 2019 novel coronavirus (SARS-CoV-2) pandemic has caused a global health emergency. The outbreak of this virus has raised a number of questions: What is SARS-CoV-2? How transmissible is SARS-CoV-2? How severely affected are patients infected with SARS-CoV-2? What are the risk factors for viral infection? What are the differences between this novel coronavirus and other coronaviruses? To answer these questions, we performed a comparative study of four pathogenic viruses that primarily attack the respiratory system and may cause death, namely, SARS-CoV-2, severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and influenza A viruses (H1N1 and H3N2 strains). This comparative study provides a critical evaluation of the origin, genomic features, transmission, and pathogenicity of these viruses. Because the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 is ongoing, this evaluation may inform public health administrators and medical experts to aid in curbing the pandemic's progression.
DOI : 10.3389/fimmu.2020.552909 Anahtar Kelimeler :
SARS-CoV-2, SARS-CoV, MERS-CoV, influenza A virus, COVID-19
ISSN: 1664-3224 Cilt: 11
Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.
AbstractGastric cancer (GC) is highly heterogeneous in the stromal and immune microenvironment, genome instability (GI), and oncogenic signatures. However, a classification of GC by combining these features remains lacking. Using the consensus clustering algorithm, we clustered GCs based on the activities of 15 pathways associated with immune, DNA repair, oncogenic, and stromal signatures in three GC datasets. We identified three GC subtypes: immunity-deprived (ImD), stroma-enriched (StE), and immunity-enriched (ImE). ImD showed low immune infiltration, high DNA damage repair activity, high tumor aneuploidy level, high intratumor heterogeneity (ITH), and frequent TP53 mutations. StE displayed high stromal signatures, low DNA damage repair activity, genomic stability, low ITH, and poor prognosis. ImE had strong immune infiltration, high DNA damage repair activity, high tumor mutation burden, prevalence of microsatellite instability, frequent ARID1A mutations, elevated PD-L1 expression, and favorable prognosis. Based on the expression levels of four genes (TAP2, SERPINB5, LTBP1, and LAMC1) in immune, DNA repair, oncogenic, and stromal pathways, we developed a prognostic model (IDOScore). The IDOScore was an adverse prognostic factor and correlated inversely with immunotherapy response in cancer. Our identification of new GC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of GCs.
DOI : 10.1038/s41698-021-00186-z
ISSN: 2397-768X Sayı: 1 Cilt: 5
Abstract Background Tumor mutation burden (TMB) has been associated with cancer immunotherapeutic response and cancer prognosis. Although many explorations have revealed that high TMB may yield many neoantigens to incite antitumor immune response, a systematic exploration of the correlation between TMB and immune signatures in different cancer types is lacking. Results We classified cancer into the lower-TMB subtype and the higher-TMB subtype for each of 32 cancer types based on their somatic mutation data from the Cancer Genome Atlas (TCGA), and compared the expression levels of immune-related genes and gene-sets between both subtypes of cancers in each cancer type. In some cancer types most of the immune signatures analyzed were upregulated in the lower-TMB subtype, while in some other cancer types the immune signatures were prone to be upregulated in the higher-TMB subtype. However, the regulatory T cells, immune cell infiltrate, tumor-infiltrating lymphocytes, and cytokine signatures tended to be upregulated in the lower-TMB subtype, and the cancer-testis antigen (CTA) and pro-inflammatory signatures were inclined to be upregulated in the higher-TMB subtype. Importantly, high TMB was associated with elevated expression of PD-L1 in diverse prevailing cancers. Furthermore, we found that higher TMB was associated with better survival prognosis in numerous cancer types while was associated with worse prognosis in a few cancer types. Conclusions High TMB may inhibit immune cell infiltrations while promote CTAs expression and inflammatory response in cancer. In many common cancer types, higher TMB may respond favorably to anti-PD-1/PD-L1 immunotherapy. Our data implicate that higher-TMB patients could gain a more favorable prognosis in diverse cancer types if treated with immunotherapy, otherwise would have a poorer prognosis compared to lower-TMB patients.
DOI : 10.1186/s12865-018-0285-5 Anahtar Kelimeler :
Tumor mutation burden, Immune signatures, Tumor immune microenvironment, Cancer immunotherapy, Cancer prognosis
ISSN: 1471-2172 Cilt: 20 Sayı: 1 Sayfa: 1 - 13
Rough set theory is based on information granules. This paper studies information granules based on a decision logic language in information tables. In this paper, the theorems of determining definable granules and definable partitions are given. Furthermore, this paper gives the definitions of the definable upper and lower approximations of indefinable granules, and studies their properties. Through the descriptions of the definable upper and lower approximations, we propose a way of describing indefinable granules. As a result, we can obtain some explicit and useful information on indefinable granules. This is then an approach to discover knowledge hidden in indefinable granules.
DOI : 10.1109/grc.2007.80 Anahtar Kelimeler :
Logic, Set theory, Muscles, Temperature, Natural languages, Problem-solving, Information analysis, data mining, formal languages, rough set theory, information granule, rough set theory, decision logic language, information table, knowledge discovery
Uncertain set is a set-valued function on an uncertainty space, and attempts to model unsharp concepts. Firstly, a definition of quadratic entropy to characterize the uncertainty of uncertain sets resulting from information deficiency is proposed. Secondly, some properties of quadratic entropy for uncertain sets are given, and the relation between quadratic entropy and Liu’s entropy of uncertain sets is discussed. Finally, a quadratic cross entropy for uncertain sets is investigated.
Without sufficient data, consulting experts is a good way to quantify unknown parameters in water resources management which will result in human uncertainty. The aim of this paper is to introduce a new tool-uncertainty theory to deal with such uncertainty which is treated as uncertain variable with uncertainty distribution. And a dependent-chance goal programming (DCGP) model is provided for water resources management under such circumstance. In the model uncertain measure is used to measure possibility that an event will occur which is maximized by minimizing the deviation (positive or negative deviation) from target of objective event under a given priority structure. In the end, the developed model is applied to a numerical example to illustrate the effectiveness of the model. The result obtained contributes to the desired water-allocation schemes for decision-markers.
Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks.
Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer. Keywords: Cancer, Microsatellite instability, Gene expression profiling, Machine learning, Algorithm, Classification
Background. PLK1 overexpression is oncogenic and is associated with poor prognosis in various cancers. However, the current PLK1 inhibitors have achieved limited clinical successes. On the other hand, although immunotherapies are demonstrating efficacy in treating many refractory cancers, a substantial number of patients do not respond to such therapies. The potential of combining PLK1 inhibition with immunotherapy for cancer treatment is worthy of exploration. Methods. We analyzed the associations of PLK1 expression with tumor immunity in 33 different cancer types. Moreover, we analyzed the associations of the drug sensitivities of PLK1 inhibitors with tumor immunity in cancer cell lines. Furthermore, we explored the mechanism underlying the significant associations between PLK1 and tumor immunity. Finally, we experimentally verified some findings from bioinformatics analysis. Results. The cancers with higher PLK1 expression levels tended to have lower immune activities, such as lower HLA expression and decreased B cells, NK cells and tumor-infiltrating lymphocytes infiltration. On the other side, elevated tumor immunity likely increased the sensitivity of cancer cells to PLK1 inhibitors. The main mechanism underlying the associations between PLK1 and tumor immunity may lie in the aberrant cell cycle and p53 pathways in cancers. Conclusions. Our findings implicate that the PLK1 inhibition and immunotherapy combination may achieve a synergistic antitumor efficacy.
Polo-like kinase 1 (PLK1) plays an important role in the initiation, maintenance, and completion of mitosis. Dysfunction of PLK1 may promote cancerous transformation and drive its progression. PLK1 overexpression has been found in a variety of human cancers and was associated with poor prognoses in cancers. Many studies have showed that inhibition of PLK1 could lead to death of cancer cells by interfering with multiple stages of mitosis. Thus, PLK1 is expected to be a potential target for cancer therapy. In this article, we examined PLK1’s structural characteristics, its regulatory roles in cell mitosis, PLK1 expression, and its association with survival prognoses of cancer patients in a wide variety of cancer types, PLK1 interaction networks, and PLK1 inhibitors under investigation. Finally, we discussed the key issues in the development of PLK1-targeted cancer therapy.
Abstract Background The Cancer Genome Atlas (TCGA) is an important data resource for cancer biologists and oncologists. However, a lack of bioinformatics expertise often hinders experimental cancer biologists and oncologists from exploring the TCGA resource. Although a number of tools have been developed for facilitating cancer researchers to utilize the TCGA data, these existing tools cannot fully satisfy the large community of experimental cancer biologists and oncologists without bioinformatics expertise. Methods We developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn) for fast and straightforward querying of TCGA “omics” data. Results TCOA provides the querying of gene expression, somatic mutations, microRNA (miRNA) expression, protein expression data based on a single molecule or cancer type. TCOA also provides the querying of expression correlation between gene pairs, miRNA pairs, gene and miRNA, and gene and protein. Moreover, TCOA provides the querying of the associations between gene, miRNA, or protein expression and survival prognosis in cancers. In addition, TCOA displays transcriptional profiles across various human cancer types based on the pan-cancer analysis. Finally, TCOA provides the querying of molecular profiles for 2877 immune-related genes in human cancers. These immune-related genes include those that are established or promising targets for cancer immunotherapy such as CTLA4, PD1, PD-L1, PD-L2, IDO1, LAG3, and TIGIT. Conclusions TCOA is a useful tool that supplies a number of unique and new functions complementary to the existing tools to facilitate exploration of the TCGA resource.
The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking. Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression. The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine–cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix–receptor interaction. Protein–protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients. This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.
Background Tumor stroma is a heterogeneous cellular component in the tumor microenvironment of breast cancer. However, very few studies have explored the identification of breast cancer subtypes based on highly heterogeneous tumor stromal signatures. Materials and Methods Using a combined dataset composed of 8 gene expression profiling datasets for breast tumor stroma, we clustered breast cancers based on the expression levels of 100 genes whose expression values were most variable across all samples. Furthermore, we investigated the molecular features of the breast cancer subtypes identified. Results We identified 2 breast cancer subtypes, termed SBCS-1 and SBCS-2. We found that the contents of stroma and immune cells were lower in SBCS-1 than in SBCS-2, while the proportion of tumor cells was higher in SBCS-1. SBCS-1 was enriched in cancer-associated pathways, including ribosomes, cell cycle, RNA degradation, RNA polymerase, DNA replication, oxidative phosphorylation, proteasome, spliceosome, and glycolysis/gluconeogenesis. SBCS-2 was enriched in pathways of graft versus host disease, type 1 diabetes mellitus, intestinal immune network for IgA production, allograft rejection, and steroid hormone biosynthesis. Moreover, many oncogenic biological processes were highly activated in SBCS-1, including proliferation, stemness, epithelial-to-mesenchymal transition (EMT), and angiogenesis. Gene co-expression network analysis identified prognostic hub genes, transcription factor encoding genes (PFDN5 and EZH2), and protein kinase encoding gene (AURKA) in a gene module highly enriched in SBCS-1. Conclusion Based on the gene expression profiles in breast cancer stroma, breast cancer can be divided into 2 subtypes, which have significantly different molecular, and clinical characteristics. The identification of new subtypes of breast cancer has clinical implications for the management of this disease.
DOI : 10.1016/j.clbc.2022.04.001 Anahtar Kelimeler :
Tumor stroma, Tumor microenvironment, Breast cancer subtypes, Gene co-expression network, Prognostic hub genes, SBCS-1 stromal breast cancer subtype 1, stromal breast cancer subtype 1, SBCS-2 stromal breast cancer subtype 2, stromal breast cancer subtype 2, TME tumor microenvironment, tumor microenvironment, NCBI National Center for Biotechnology Information, National Center for Biotechnology Information, TCGA The Cancer Genome Atlas, The Cancer Genome Atlas, GDC Genomic Data Commons, Genomic Data Commons, RSEM RNA-Seq by expectation-maximization, RNA-Seq by expectation-maximization, ssGSEA single-sample gene-set enrichment analysis, single-sample gene-set enrichment analysis, TILs tumor-infiltrating lymphocytes, tumor-infiltrating lymphocytes, GSEA gene set enrichment analysis, gene set enrichment analysis, EMT epithelial-mesenchymal transition, epithelial-mesenchymal transition, WGCNA weighted correlation network analysis, weighted correlation network analysis, KEGG Kyoto Encyclopedia of Genes and Genomes, Kyoto Encyclopedia of Genes and Genomes, TF transcription factor, transcription factor, RFS recurrence-free survival, recurrence-free survival
Aims Long non-coding RNAs (lncRNAs) are associated with cancer development, while their relationship with the cancer-associated stromal components remains poorly understood. In this review, we performed a broad description of the functional landscape of stroma-associated lncRNAs in various cancers and their roles in regulating the tumor-stroma crosstalk. Materials and methods We carried out a systematic literature review of PubMed, Scopus, Medline, Bentham, and EMBASE (Elsevier) databases by using the keywords “LncRNAs in cancer,” “LncRNAs in tumor stroma,” “stroma,” “cancer-associated stroma,” “stroma in the tumor microenvironment,” “tumor-stroma crosstalk,” “drug resistance of stroma,” and “stroma in immunosuppression” till July 2020. We collected the latest articles addressing the biological functions of stroma-associated lncRNAs in cancer. Key findings These articles reported that dysregulated stroma-associated lncRNAs play significant roles in modulating the tumor microenvironment (TME) by the regulation of tumor-stroma crosstalk, epithelial to mesenchymal transition (EMT), endothelial to mesenchymal transition (EndMT), extracellular matrix (ECM) turnover, and tumor immunity. Significance The tumor stroma is a substantial portion of the TME, and the dysregulation of tumor stroma-associated lncRNAs significantly contributes to cancer initiation, progression, angiogenesis, immune evasion, metastasis, and drug resistance. Thus, stroma-associated lncRNAs could be potentially useful targets for cancer therapy.
DOI : 10.1016/j.lfs.2020.118725 Anahtar Kelimeler :
Long non-coding RNAs, Tumor-stroma crosstalk, Tumor stroma, Tumor microenvironment, Cancer therapy
ISSN: 0024-3205 Cilt: 264 Sayfa: 118725
Regression analysis is a statistical process for estimating the relationships among variables based on probability. Because not all the imprecise quantities can be described by random variables, it is necessary to investigate relationships between an uncertain variable and some other variables. In this paper, an uncertain linear regression model is established based on uncertainty theory. Then, the estimators of parameters are obtained in the proposed model by the empirical uncertainty distribution coming from experts’ experimental data. Finally, the uncertain linear regression model is applied to solve an estimate problem.
DOI : 10.1007/s10845-014-1022-4 Anahtar Kelimeler :
Uncertainty theory, Uncertainty distribution, Uncertain statistics, Linear regression model
ISSN: 0956-5515 1572-8145 Sayı: 3 Cilt: 28 Sayfa: 559-564
Prostate cancer (PC) is heterogeneous in the tumor immune microenvironment (TIME). Subtyping of PC based on the TIME could provide new insights into intratumor heterogeneity and its correlates of clinical features. Based on the enrichment scores of 28 immune cell types in the TIME, we performed unsupervised clustering to identify immune-specific subtypes of PC. The clustering analysis was performed in ten different bulk tumor transcriptomic datasets and in a single-cell RNA-Seq (scRNA-seq) dataset, respectively. We identified two PC subtypes: PC immunity high (PC-ImH) and PC immunity low (PC-ImL), consistently in these datasets. Compared to PC-ImL, PC-ImH displayed stronger immune signatures, worse clinical outcomes, higher epithelial-mesenchymal transition (EMT) signature, tumor stemness, intratumor heterogeneity (ITH) and genomic instability, and lower incidence of TMPRSS2-ERG fusion. Tumor mutation burden (TMB) showed no significant difference between PC-ImH and PC-ImL, while copy number alteration (CNA) was more significant in PC-ImL than in PC-ImH. PC-ImH could be further divided into two subgroups, which had significantly different immune infiltration levels and clinical features. In conclusion, “hot” PCs have stronger anti-tumor immune response, while worse clinical outcomes versus “cold” PCs. CNA instead of TMB plays a crucial role in the regulation of TIME in PC. TMPRSS2-ERG fusion correlates with decreased anti-tumor immune response while better disease-free survival in PC. The identification of immune-specific subtypes has potential clinical implications for PC immunotherapy.
Water Saving Management Contract (WSMC) is a new market-oriented mechanism established for sharing benefits from water conservation of clients. It is supported by cooperation between government and nongovernmental capital, and implemented by water saving service company (WSSC). Being at the exploratory stage, the WSMC industry in China is faced with many risks that restrict its development seriously. In order to help figure out the most significant risks, this paper develops integrated evaluation and prioritization of risk categories and risk factors. First, the WSMC project process is divided into five stages based on life cycle theory. Then, a risk set which contains 29 factors in 12 categories is summarized. Subsequently, to determine the weights of each stage and each factor, a pairwise comparison-based best-worst method (BWM) is adopted, in which the preference vectors are obtained by an expert panel with extensive industry expertise. Furthermore, the global weights are applied to ranking and prioritization of all the risks. Results show that contract risk mainly caused by contract negotiation and signing is regarded to have a highest influence on project success. However, water saving effect evaluation risk ranks the lowest. Finally, risk mitigation strategies and policies for stakeholders, main contributions, limitations of current study and further research topics are described.
DOI : 10.1016/j.jclepro.2021.127153 Anahtar Kelimeler :
Water saving management contract (WSMC), Project life cycle, Best-worst method (BWM), Risk identification, Risk prioritization
ISSN: 0959-6526 Cilt: 306 Sayfa: 127153
The transmembrane serine protease 2 (TMPRSS2) is a key molecule for SARS-CoV-2 invading human host cells. To provide insights into SARS-CoV-2 infection of various human tissues and understand the potential mechanism of SARS-CoV-2 infection, we investigated TMPRSS2 expression in various normal human tissues and SARS-CoV-2-infected human tissues. Using publicly available datasets, we performed computational analyses of TMPRSS2 expression levels in 30 normal human tissues, and compared them between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) populations in these tissues. We found that TMPRSS2 expression levels were the highest in the prostate, stomach, pancreas, lungs, small intestine, and salivary gland. The TMPRSS2 protein had relatively high expression levels in the parathyroid gland, stomach, small intestine, pancreas, kidneys, seminal vesicle, epididymis, and prostate. However, TMPRSS2 expression levels were not significantly different between females and males or between younger and older populations in these tissues. The pathways enriched in TMPRSS2-upregulated pan-tissue were mainly involved in immune, metabolism, cell growth and proliferation, stromal signatures, and cancer and other diseases. Many cytokine genes displayed positive expression correlations with TMPRSS2 in pan-tissue, including TNF-α, IL-1, IL-2, IL-4, IL-7, IL-8, IL-12, IL-18, IFN-α, MCP-1, G-CSF, and IP-10. We further analyzed TMPRSS2 expression levels in nasopharyngeal swabs from SARS-CoV-2-infected patients. TMPRSS2 expression levels showed no significant difference between males and females or between younger and older patients. However, they were significantly lower in SARS-CoV-2-infected than in healthy individuals and patients with other viral acute respiratory illnesses. Interestingly, TMPRSS2 expression levels were positively correlated with the enrichment levels of four immune signatures (B cells, CD8+ T cells, NK cells, and interferon response) in SARS-CoV-2-infected patients but likely to be negatively correlated with them in the normal lung tissue. Our data may provide insights into the mechanism of SARS-CoV-2 infection.
The aberrant expression of microRNAs (miRNAs) and genes in tumor microenvironment (TME) has been associated with the pathogenesis of colon cancer. An integrative exploration of transcriptional markers (gene signatures) and miRNA−mRNA regulatory networks in colon tumor stroma (CTS) remains lacking. Using two datasets of mRNA and miRNA expression profiling in CTS, we identified differentially expressed miRNAs (DEmiRs) and differentially expressed genes (DEGs) between CTS and normal stroma. Furthermore, we identified the transcriptional markers which were both gene targets of DEmiRs and hub genes in the protein−protein interaction (PPI) network of DEGs. Moreover, we investigated the associations between the transcriptional markers and tumor immunity in colon cancer. We identified 17 upregulated and seven downregulated DEmiRs in CTS relative to normal stroma based on a miRNA expression profiling dataset. Pathway analysis revealed that the downregulated DEmiRs were significantly involved in 25 KEGG pathways (such as TGF-β, Wnt, cell adhesion molecules, and cytokine−cytokine receptor interaction), and the upregulated DEmiRs were involved in 10 pathways (such as extracellular matrix (ECM)-receptor interaction and proteoglycans in cancer). Moreover, we identified 460 DEGs in CTS versus normal stroma by a meta-analysis of two gene expression profiling datasets. Among them, eight upregulated DEGs were both hub genes in the PPI network of DEGs and target genes of the downregulated DEmiRs. We found that three of the eight DEGs were negative prognostic factors consistently in two colon cancer cohorts, including COL5A2, EDNRA, and OLR1. The identification of transcriptional markers and miRNA−mRNA regulatory networks in CTS may provide insights into the mechanism of tumor immune microenvironment regulation in colon cancer.
Aims The crosstalk between cancer cells and nerves plays an important role in tumor biology. However, the correlation between the neurotrophin signaling (NS) and anti-tumor immunity and immunotherapy response in cancer remains unexplored. Materials and methods We analyzed associations of NS with anti-tumor immune signatures, tumor immunity-related molecular and genomic features, and clinical features in 33 TCGA cancer types. We also explored the association between NS and the response to immune checkpoint inhibitors (ICIs) in four cancer cohorts. Key findings NS scores had significant positive correlations with the enrichment scores of anti-tumor immune signatures, including CD8+ T cells, interferon response, natural killer cells, Toll-like receptor and NOD-like receptor signaling pathways in most cancer types. NS scores were inversely correlated with the scores of DNA damage repair pathways, tumor mutation burden, copy number alterations, intra-tumor heterogeneity, and tumor stemness in diverse cancers. In contrast, NS scores were significantly and positively correlated with the apoptosis pathway's scores in 32 of the 33 cancer types. NS scores were significantly lower in early-stage versus late-stage and in primary versus metastatic tumors in diverse cancers. Higher NS scores were correlated with better survival in pan-cancer and in eight individual cancer types. Moreover, the response rate to ICIs was higher in higher-NS-score than in lower-NS-score tumors in four cancer cohorts. Elevated NS was correlated with increased drug sensitivity for numerous anti-tumor targeted drugs. Significance NS is a positive biomarker for anti-tumor immune response, prognosis, and the response to targeted and immunotherapeutic drugs in cancer.
Uncertain linear regression (ULR) model based on symmetric triangular uncertain set has been studied early. This paper extends the symmetric triangular uncertain coefficients to asymmetric triangular uncertain coefficients and builds two methods for estimating the parameters of ULR model. Our aim is to minimize the differences of the uncertain membership functions between the observed and estimated values. Firstly, we propose a linear programming method, whose principle is to minimize the sum of the absolute values of the differences between left width and right width of two triangular uncertain sets for each index i. Secondly, we develop a new nonlinear programming method by maximizing the overlaps of acreage of the estimated and real triangular uncertain sets in a particular \(h_i\)-cut. Then, a criterion is established to evaluate the performance of the proposed approaches. Finally, we use an example based on industrial water demand data of China to illustrate our proposed approaches which are reasonable and compare the explanatory power of the ULR model and traditional linear regression (TLR) model using the presented evaluation criteria, which shows that the performance of the ULR model is obviously better than the TLR model.