Biomedical subjects

Shuta Tomida

Publications and source records attributed to Shuta Tomida.

13 recordsLinked to original sources

PubMed2025

Cancer-associated fibroblast-derived SOD3 enhances lymphangiogenesis to drive metastasis in lung adenocarcinoma.

Despite advancements in diagnostic and therapeutic strategies, lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality due to its aggressive metastatic potential. Extracellular superoxide dismutase (SOD3) is an antioxidant enzyme that regulates oxidative stress and is regarded as a tumor suppressor. However, studies have demonstrated that SOD3 can either promote or inhibit cell proliferation and survival in various cancers, and its molecular mechanisms within the tumor microenvironment are poorly understood. In this study, we report a breakthrough in uncovering the role of SOD3 derived from cancer-associated fibroblasts (CAFs) in LUAD. Using LUAD xenograft models co-implanted with SOD3-overexpressing CAFs (CAFSOD3), we observe an aggressive tumor phenotype characterized by increased lymphangiogenesis and lymphatic vessel invasion (LVI) of the tumor. Additionally, LUAD patients with elevated SOD3 levels exhibit a higher incidence of LVI and metastasis. Notably, RNA sequencing of CAFSOD3 reveals that SOD3-mediated VEGF-dependent tumor progression and lymphangiogenesis are up-regulated. Furthermore, single-cell transcriptomic analysis of LUAD clinical samples confirms a strong correlation between SOD3 expression in fibroblasts and characteristics of tumor exacerbation, such as lymphangiogenesis and metastasis. These findings underscore new insights into the role of CAF-derived SOD3 in LUAD progression and highlight its potential as a biomarker and therapeutic target.

Lymphangiogenesis↗

PubMed2024

Soluble immune checkpoint factors reflect exhaustion of antitumor immunity and response to PD-1 blockade.

BACKGROUNDPrecise stratification of patients with non-small cell lung cancer (NSCLC) is needed for appropriate application of PD-1/PD-L1 blockade therapy.METHODSWe measured soluble forms of the immune-checkpoint molecules PD-L1, PD-1, and CTLA-4 in plasma of patients with advanced NSCLC before PD-1/PD-L1 blockade. A prospective biomarker-finding trial (cohort A) included 50 previously treated patients who received nivolumab. A retrospective observational study was performed for patients treated with any PD-1/PD-L1 blockade therapy (cohorts B and C), cytotoxic chemotherapy (cohort D), or targeted therapy (cohort E). Plasma samples from all patients were assayed for soluble immune-checkpoint molecules with a highly sensitive chemiluminescence-based assay.RESULTSNonresponsiveness to PD-1/PD-L1 blockade therapy was associated with higher concentrations of these soluble immune factors among patients with immune-reactive (hot) tumors. Such an association was not apparent for patients treated with cytotoxic chemotherapy or targeted therapy. Integrative analysis of tumor size, PD-L1 expression in tumor tissue (tPD-L1), and gene expression in tumor tissue and peripheral CD8+ T cells revealed that high concentrations of the 3 soluble immune factors were associated with hyper or terminal exhaustion of antitumor immunity. The combination of soluble PD-L1 (sPD-L1) and sCTLA-4 efficiently discriminated responsiveness to PD-1/PD-L1 blockade among patients with immune-reactive tumors.CONCLUSIONCombinations of soluble immune factors might be able to identify patients unlikely to respond to PD-1/PD-L1 blockade as a result of terminal exhaustion of antitumor immunity. Our data suggest that such a combination better predicts, along with tPD-L1, for the response of patients with NSCLC.TRIAL REGISTRATIONUMIN000019674.FUNDINGThis study was funded by Ono Pharmaceutical Co. Ltd. and Sysmex Corporation.

Humans↗

PubMed2006

Expression profile-defined classification of lung adenocarcinoma shows close relationship with underlying major genetic changes and clinicopathologic behaviors.

PURPOSE: This study was conducted to gain insight into the relationship between expression profiles and underlying genetic changes, which are known to be important for the pathogenesis of lung cancers. METHODS: Expression profiles of 18,175 unique genes and three major targets for genetic changes, p53, epidermal growth factor receptor (EGFR), and K-ras, were investigated in 149 patients with non-small-cell lung cancer, including 90 patients with adenocarcinoma to determine their relationships with various clinicopathologic features and Gene Ontology (GO) terms. RESULTS: This study successfully established a basis for expression profile-defined classification, which can classify adenocarcinomas into two major types, terminal respiratory unit (TRU) type and non-TRU type. Our GO term-based identifier of particular biologic processes, molecular functions, and cellular compartments clearly showed characteristic retention of normal peripheral lung features in TRU type, in sharp contrast to the significant association of non-TRU type with cell cycling and proliferation-related features. While significantly higher frequency of EGFR mutation was observed in TRU type, we found that the presence of EGFR mutations was a significant predictor of shorter postoperative survival for TRU type, independent of disease stage. We were also able to identify a set of genes in vivo with significant upregulation in the presence of EGFR mutations. CONCLUSION: This study has shed light on heterogeneity in lung cancers, especially in adenocarcinomas, by establishing a molecularly, genetically, and clinically relevant, expression profile-defined classification. Future studies using independent patient cohorts are warranted to confirm the prognostic significance of EGFR mutations in TRU-type adenocarcinoma.

Adenocarcinoma↗

PubMed2005

A polycistronic microRNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation.

MicroRNAs (miRNAs) are small noncoding RNAs, thought to be involved in physiologic and developmental processes by negatively regulating expression of target genes. We have previously reported frequent down-regulation of the let-7 miRNA family in lung cancers and, in the present study, assessed alteration in a panel of 19 lung cancer cell lines. As a result, we found for the first time that the miR-17-92 cluster, which comprises seven miRNAs and resides in intron 3 of the C13orf25 gene at 13q31.3, is markedly overexpressed in lung cancers, especially with small-cell lung cancer histology. Southern blot analysis revealed the presence of increased gene copy numbers of the miRNA cluster in a fraction of lung cancer cell lines with overexpression. In addition, we were able to show predominant localization of C13orf25 transcripts within the nucleus and introduction of the expression construct of the miR-17-92 cluster, but not the putative open reading frame of C13orf25, enhancing lung cancer cell growth. These findings clearly suggest that marked overexpression of the miR-17-92 cluster with occasional gene amplification may play a role in the development of lung cancers, especially in their most aggressive form, small-cell lung cancer, and that the C13orf25 gene may well be serving as a vehicle in this regard.

Base Sequence↗

PubMed2005

Reduced expression of Dicer associated with poor prognosis in lung cancer patients.

Emerging evidence suggests that microRNA, which are well-conserved, abundant and small regulatory RNA, may be involved in the pathogenesis of human cancers. We recently reported that expression of let-7 was frequently reduced in lung cancers, and that reduced let-7 expression was significantly associated with shorter patient survival. Two members of the double-stranded RNA-specific endonuclease family, Dicer and Drosha, convert precursor forms of microRNA into their mature forms using a stepwise process. In the present study, we examined expression levels of these genes in 67 non-small cell lung cancer cases, and found for the first time that Dicer expression levels were reduced in a fraction of lung cancers with a significant prognostic impact on the survival of surgically treated cases. It should be noted that multivariate COX regression analysis showed that the prognostic impact of Dicer (P=0.001) appears to be independent of disease stage (P=0.001), while logistic regression analysis demonstrated that the higher incidence of reduced Dicer expression in poorly differentiated tumors remained significant even after correction for other parameters (P=0.02). Given the fundamental and multiple biological roles of Dicer in various cellular processes, our results suggest the involvement of reduced Dicer expression in the development of lung cancers, thus warranting further investigations of the underlying mechanisms, which can be expected to enhance understanding of the molecular pathogenesis of this fatal cancer.

Adult↗

PubMed2005

Throwing new light on lung cancer pathogenesis: updates on three recent topics.

Lung cancers have become the leading cause of cancer deaths in Japan, claiming more than 55 000 lives annually. Unfortunately, substantial improvement in terms of cure rates has not been achieved over the last two decades, although during the same period of time in-depth basic knowledge of the molecular mechanisms, which underlies carcinogenesis and progression of this deadly group of neoplasms, has accumulated at an amazing pace. It has consequently become evident that they have many shared but also distinct features, when comparisons are made not only with other common epithelial cancers of adults, such as colon cancer, but also within the various histologic types of lung cancers themselves. This review article provides an up-date on cutting-edge research into the following three different topics, from which important new insights have been obtained. The first concerns genetic instability, especially chromosome instability, and checkpoint failure in lung cancers. Second, we deal with EGFR mutations, which shows revealing specificities in various aspects. Finally, advances in the expression profiling analysis of both transcriptomes and proteomes of lung cancers are summarized.

Adenocarcinoma↗

PubMed2004

Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma.

BACKGROUND: Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease. RESULTS: To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs. CONCLUSION: ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients.

Alleles↗

PubMed2004

Gene expression-based, individualized outcome prediction for surgically treated lung cancer patients.

Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8644 genes in 50 non-small-cell lung cancer (NSCLC) cases, which had been consecutively operated on within a defined short period of time and followed up for more than 5 years. The resultant classifier of NSCLCs yielded 82% accuracy for forecasting survival or death 5 years after surgery of a given patient. In addition, since two major histologic classes may differ in terms of outcome-related expression signatures, histologic-type-specific outcome classifiers were also constructed. The resultant highly predictive classifiers, designed specifically for nonsquamous cell carcinomas, showed a prediction accuracy of more than 90% independent of disease stage. In addition to the presence of heterogeneities in adenocarcinomas, our unsupervised hierarchical clustering analysis revealed for the first time the existence of clinicopathologically relevant subclasses of squamous cell carcinomas with marked differences in their invasive growth and prognosis. This finding clearly suggests that NSCLCs comprise distinct subclasses with considerable heterogeneities even within one histologic type. Overall, these findings should advance not only our understanding of the biology of lung cancer but also our ability to individualize postoperative therapies based on the predicted outcome.

Adenocarcinoma↗

PubMed2004

Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival.

In this study, we report for the first time reduced expression of the let-7 microRNA in human lung cancers. Interestingly, 143 lung cancer cases that had undergone potentially curative resection could be classified into two major groups according to let-7 expression in unsupervised hierarchical analysis, showing significantly shorter survival after potentially curative resection in cases with reduced let-7 expression (P = 0.0003). Multivariate COX regression analysis showed this prognostic impact to be independent of disease stage (hazard ratio = 2.17; P = 0.009). In addition, overexpression of let-7 in A549 lung adenocarcinoma cell line inhibited lung cancer cell growth in vitro. This study represents the first report of reduced expression of let-7 and the potential clinical and biological effects of such a microRNA alteration.

Adult↗

PubMed2004

Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction.

PURPOSE: Recently, several expression-profiling experiments have shown that adenocarcinoma can be classified into subgroups that also reflect patient survival. In this study, we examined the expression patterns of 44 genes selected by these studies to test whether their expression patterns were relevant to prognosis in our cohort as well, and to create a prognostic model applicable to clinical practice. PATIENTS AND METHODS: Expression levels were determined in 85 adenocarcinoma patients by quantitative reverse transcriptase polymerase chain reaction. Cluster analysis was performed, and a prognostic model was created by the proportional hazards model using a stepwise method. RESULTS: Hierarchical clustering divided the cases into three major groups, and group B, comprising 21 cases, had significantly poor survival (P =.0297). Next, we tried to identify a smaller number of genes of particular predictive value, and eight genes (PTK7, CIT, SCNN1A, PGES, ERO1L, ZWINT, and two ESTs) were selected. We then calculated a risk index that was defined as a linear combination of gene expression values weighted by their estimated regression coefficients. The risk index was a significant independent prognostic factor (P =.0021) by multivariate analysis. Furthermore, the robustness of this model was confirmed using an independent set of 21 patients (P =.0085). CONCLUSION: By analyzing a reasonably small number of genes, patients with adenocarcinoma could be stratified according to their prognosis. The prognostic model could be applicable to future decisions concerning treatment.

Adenocarcinoma↗

PubMed2003

Inference of common genetic network using fuzzy adaptive resonance theory associated matrix method.

Inferring genetic networks from gene expression data is the most challenging work in the post-genomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we developed the fuzzy adaptive resonance theory associated matrix (F-ART matrix) method to infer genetic networks and applied it to experimental time series data, which are gene expression profiles of Saccharomyces cerevisiae responding under oxidative stresses such as diamide, heat shock and H2O2. We preprocessed them using the fuzzy adaptive resonance theory and successfully identified genetic interactions by drawing a 2-dimensional matrix. The identified interactions between diamide and heat shock stress were confirmed to be the common interactions for two stresses, compared with the KEGG metabolic map, BRITE protein interaction map, and gene interaction data of other papers. In the predicted common genetic network, the hit ratio was 60% for the KEGG map. Several gene interactions were also drawn, which have been reported to be important in oxidative stress. This result suggests that F-ART matrix has the potential to function as a new method to extract the common genetic networks of two different stresses using experimental time series microarray data.

Journal Article↗

PubMed2002

Artificial neural network predictive model for allergic disease using single nucleotide polymorphisms data.

The purpose of this study was to develop a novel diagnostic prediction method for allergic diseases from the data of single nucleotide polymorphisms (SNPs) using an artificial neural network (ANN). We applied the prediction method to four allergic diseases, such as atopic dermatitis (AD), allergic conjunctivitis (AC), allergic rhinitis (AR) and bronchial asthma (BA), and verified its predictive ability. Almost all the learning data were precisely predicted. Regarding the evaluation data, the learned ANN model could correctly predict a diagnosis with more than 78% accuracy. We also analyzed the SNP data using multiple regression analysis (MRA). Using the MRA model, less than 10% of patients with the above allergic diseases were correctly diagnosed, while this figure was more than 75% for persons without allergic diseases. From these results, it was shown that the ANN model was superior to the MRA model with respect to predictive ability of allergic diseases. Moreover, we used two different methods to convert the genetic polymorphism data into numerical data. Using both methods, diagnostic predictions were quite precise and almost the same predictive abilities were observed. This is the first study showing the application and usefulness of an ANN for the prediction of allergic diseases based on SNP data.

Journal Article↗

PubMed2002

Analysis of expression profile using fuzzy adaptive resonance theory.

MOTIVATION: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY: The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html

Algorithms↗