Expression profiles of two types of human knee-joint cartilage

Ochi, Kensuke; Daigo, Yataro; Katagiri, Toyomasa; Saito-Hisaminato, Akihiko; Tsunoda, Tatsuhiko; Toyama, Yoshiaki; Matsumoto, Hideo; Nakamura, Yusuke

doi:10.1007/s10038-003-0004-8

Cited by 35 publications

(44 citation statements)

References 23 publications

(26 reference statements)

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“…Possible microarray studies with additional genes may provide some insight in the defi ning characteristics of the various cartilage types. However, such an approach should be undertaken carefully and take into account pre-existing biological information as screening a massive number of genes may yield results that are diffi cult to interpret and may overlook known relevant genes (Ochi et al, 2003). Differences will likely exist for human samples or for adult and osteoarthritic tissues as well compared to the bovine samples presented here.…”

Section: Me Levenston Et Al Discrimination Of Meniscal Cell Phenotypesmentioning

confidence: 99%

Discrimination of meniscal cell phenotypes using gene expression profiles

Son

Levenston²

2012

eCM

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The lack of quantitative and objective metrics to assess cartilage and meniscus cell phenotypes contributes to the challenges in fi brocartilage tissue engineering. Although functional assessment of the fi nal resulting tissue is essential, initial characterization of cell sources and quantitative description of their progression towards the natural, desired cell phenotype would provide an effective tool in optimizing cell-based tissue engineering strategies. The purpose of this study was to identify quantifi able characteristics of meniscal cells and thereby fi nd phenotypical markers that could effectively categorize cells based on their tissue of origin (cartilage, inner, middle, and outer meniscus). The combination of gene expression ratios collagen VI/collagen II, ADAMTS-5/collagen II, and collagen I/collagen II was the most effective indicator of variation among different tissue regions. We additionally demonstrate a possible application of these quantifi able metrics in evaluating the use of serially passaged chondrocytes as a possible cell source in fi brocartilage engineering. Comparing the ratios of the passaged chondrocytes and the native meniscal cells may provide direction to optimize towards the desired cell phenotype. We have thus shown that measurable markers defi ning the characteristics of the native meniscus can establish a standard by which different tissue engineering strategies can be objectively assessed. Such metrics could additionally be useful in exploring the different stages of meniscal degradation in osteoarthritis and provide some insight in the disease progression.

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Section: Me Levenston Et Al Discrimination Of Meniscal Cell Phenotypesmentioning

confidence: 99%

Discrimination of meniscal cell phenotypes using gene expression profiles

Son

Levenston²

2012

eCM

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“…Through the gene expression profile analysis of 15 SCLCs and 86 NSCLCs coupled with purification of cancer cell population by laser microdissection on a cDNA microarray consisting of 27,648 genes, and their comparison with the expression profile data of 31 normal human tissues (27 adult and 4 fetal organs; refs. 15,16), we identified a number of potential molecular targets for diagnosis, treatment, and/or choice of therapy (8 -12). To verify the biological and clinicopathologic significance of the respective gene products, we have also been performing high-throughput screening of loss-of-function effects by means of the RNA interference technique as well as tumor tissue microarray analysis of clinical lung cancer materials (17 -30).…”

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confidence: 99%

Activation of KIF4A as a Prognostic Biomarker and Therapeutic Target for Lung Cancer

Taniwaki

Takano

Ishikawa

et al. 2007

Clinical Cancer Research

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137

116

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Purpose and Experimental Design: To identify molecules that might be useful as diagnostic/ prognostic biomarkers and as targets for the development of new molecular therapies, we screened genes that were highly transactivated in a large proportion of 101lung cancers by means of a cDNA microarray representing 27,648 genes. We found a gene encoding KIF4A, a kinesin family member 4A, as one of such candidates. Tumor tissue microarray was applied to examine the expression of KIF4A protein and its clinicopathologic significance in archival non^small cell lung cancer (NSCLC) samples from 357 patients. A role of KIF4A in cancer cell growth and/or survival was examined by small interfering RNA experiments. Cellular invasive activity of KIF4A on mammalian cells was examined using Matrigel assays. Results: Immunohistochemical staining detected positive KIF4A staining in 127 (36%) of 357 NSCLCs and 19 (66%) of 29 small-cell lung cancers examined. Positive immunostaining of KIF4A protein was associated with male gender (P = 0.0287), nonadenocarcinoma histology (P = 0.0097), and shorter survival for patients with NSCLC (P = 0.0005), and multivariate analysis confirmed its independent prognostic value (P = 0.0012). Treatment of lung cancer cells with small interfering RNAs for KIF4A suppressed growth of the cancer cells. Furthermore, we found that induction of exogenous expression of KIF4A conferred cellular invasive activity on mammalian cells. Conclusions: These data strongly implied that targeting the KIF4A molecule might hold a promise for the development of anticancer drugs and cancer vaccines as well as a prognostic biomarker in clinic.

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“…cDNAs spotted on the glass slides were prepared by RT-PCR using a unique primer set that corresponded to cDNA sequence of each gene as described previously (8,20). The PCR products were spotted on type VII glass-slides (Amersham Biosciences, Uppsala, Sweden) with a Microarray Spotter Generation III (Amersham Biosciences); 4,608 genes were spotted in duplicate on a single slide.…”

Section: Methodsmentioning

confidence: 99%

“…We prepared five different sets of slides (a total of 23,040 spots), each of which contained the same 52 housekeeping genes as well as two non-human negative control genes. Hybridization, washing and detection of signals were carried out as described previously (8,(18)(19)(20)(21). The fluorescence intensities of Cy5 (tumor) and Cy3 (control) for each target spot were adjusted so that the mean Cy3/Cy5 ratio of the 52 housekeeping genes was equal to one.…”

Section: Methodsmentioning

confidence: 99%

“…We applied a hierarchical clustering method (8,20,21) to both genes and 53 lung tumors (16 metastatic lung ADC and an independent set of 37 primary sites of NSCLC analyzed previously) (8). To obtain reproducible clusters for classification of these 53 samples, we selected 4,423 genes for which valid data were obtained in 90% of the experiments, and whose expression ratios varied by standard deviations of >0.8.…”

Section: Methodsmentioning

confidence: 99%

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Expression profiles of metastatic brain tumor from lung adenocarcinomas on cDNA microarray

Kikuchi

Daigo²,

Ishikawa³

et al. 2006

Int J Oncol

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Abstract. Distant metastasis is one of the crucial parameters determining the type of treatment and prognosis of patients. Previous studies discovered important factors involved in multiple steps of metastasis, the precise mechanisms of metastasis still remain to be clarified. To identify genes associated with this complicated biological feature of cancer, we analyzed expression profiles of 16 metastatic brain tumors derived from primary lung adenocarcinoma (ADC) using cDNA microarray representing 23,040 genes. We applied bioinformatic algorithm to compare the expression data of these 16 brain metastatic loci with those of 37 primary NSCLCs including 22 ADCs, and found that metastatic tumor cells has very different characteristics of gene expression patterns from primary ones. Two hundred and forty-four genes that showed significantly different expression levels between the two groups included plasma membrane bounding proteins, cellular antigens, and cytoskeletal proteins that might play important roles in altering cell-cell communication, attachment, and cell motility, and enhance the metastatic ability of cancer cells. Our results provide valuable information for development of predictive markers as well as novel therapeutic target molecules for metastatic brain tumor of ADC of the lung. IntroductionDistant metastasis is one of crucial steps in tumor development and progression causing difficulty in treatment and resulting in patient death (1,2). Since there is no curative therapy for metastatic tumors at present, novel strategies for diagnosis at an earlier stage and better therapies to improve patient survival with metastatic cancer are urgently required. Distant metastasis is considered to occur as multiple-step events which include invasion from a primary site, penetration into blood or lymphatic flow, circulation to reach distant organs, extravasation into a target organ, and re-growth (3,4). To clarify this complicated but clinically important metastatic process, many investigators have attempted to identify the molecules of importance in these steps, but precise mechanisms of metastasis still remain largely unknown (5-7).Because microarray technology allows us to analyze expression level of thousands of genes simultaneously, we previously applied this technology to characterize nonsmall cell lung carcinomas (NSCLCs), which include adenocarcinoma (ADC) and squamous cell carcinoma (SCC), and indicated that biological characteristics of each lung cancer like the risk of lymph node metastasis could be predictable by the expression profile of the tumor cells (8). In addition to the lymph node metastasis, lung cancer is known to show higher rate of brain metastasis; nearly 30% of metastatic brain tumors are derived from primary lung cancer. Lung ADC is generally characterized by the early development of brain metastasis, that is observed even in some cases whose primary tumor is symptom-free (9). The incidence of brain metastasis, based on autopsy findings, is as high as 50% in patients with ADC (10,11). Few ...

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Expression profiles of two types of human knee-joint cartilage

Cited by 35 publications

References 23 publications

Discrimination of meniscal cell phenotypes using gene expression profiles

Discrimination of meniscal cell phenotypes using gene expression profiles

Activation of KIF4A as a Prognostic Biomarker and Therapeutic Target for Lung Cancer

Expression profiles of metastatic brain tumor from lung adenocarcinomas on cDNA microarray

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