Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention ...of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings.
We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression.
The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m
) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m
). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (
= 0.323).
The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.
The utility of cancer cell lines depends largely on their accurate classification, commonly based on histopathological diagnosis of the cancers from which they were derived. However, because cancer ...is often heterogeneous, the cell line, which also has the opportunity to alter in vitro, may not be representative. Yet without the overall architecture used in histopathological diagnosis of fresh samples, reclassification of cell lines has been difficult. Gene-expression profiling accurately reproduces histopathological classification and is readily applicable to cell lines. Here, we compare the gene-expression profiles of 41 cell lines with 44 tumors from lung cancer. These profiles were generated after hybridization of samples to four replicate 7,685-element cDNA microarrays. After removal of genes that were uniformly up- or down-regulated in fresh compared with cell-line samples, cluster analysis produced four major branch groups. Within these major branches, fresh tumor samples essentially clustered according to pathological type, and further subclusters were seen for both adenocarcinoma (AC) and small cell lung carcinoma (SCLC). Four of eight squamous cell carcinoma (SCC) cell lines clustered with fresh SCC, and 11 of 13 SCLC cell lines grouped with fresh SCLC. In contrast, although none of the 11 AC cell lines clustered with AC tumors, three clustered with SCC tumors and six with SCLC tumors. Although it is possible that preexisting SCC or SCLC cells are being selected from AC tumors after establishment of cell lines, we propose that, even in situ, AC will ultimately progress toward one of two poorly differentiated phenotypes with expression profiles resembling SCC or SCLC.
Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. To make a clinically useful classification by gene ...expression profiling, we used a 40 386 element cDNA microarray to analyse 48 SCC, nine adenocarcinoma, and 30 normal lung samples. Initial analysis by hierarchical clustering (HC) allowed division of SCCs into two distinct subclasses. An additional independent round of HC induced a similar partition and consensus clustering with the non-negative matrix factorization approach indicated the robustness of this classification. Kaplan-Meier analysis with the log-rank test pointed to a nonsignificant difference in survival (P = 0.071), but the likelihood of survival to 6 years was significantly different between the two groups (40.5 vs 81.8%, P = 0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and upregulation of cell-proliferation-related genes was evident in the subclass with poor prognosis. In the subclass with better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were upregulated. This work represents an important step toward the identification of clinically useful classification for lung SCC.
Problem
To investigate the molecular mechanism of endometriosis, gene expression profiling was analyzed in a rat endometriosis model.
Method of study
An endometriosis model was induced by uterine ...autotransplantation in the peritoneal cavity on a female‐SD rat (8 weeks old). As control samples, the normal uterine tissues were used. The gene expression was compared between endometriotic lesions and normal uterine tissues by cDNA microarray analysis, quantitative real time RT‐PCR and immunohistochemistry.
Results
The expression of 71 genes was upregulated and that of 45 genes was downregulated in the endometriotic lesions compared to normal uterine tissues. The upregulated genes included genes encoding cytokines, chemokines, growth factors and cell adhesion molecules. The levels of transcripts of osteopontin, Lyn, Vav1, Runx1, and l‐selectin in the endometriotic lesions were 130, 10, 10, 12 and 46‐fold higher than the respective levels in the eutopic endometrial samples.
Conclusion
The results suggest that osteopontin, Lyn, Vav1, Runx1, and l‐selectin play important roles in the pathogenesis of endometriosis.
Butyrate is a well known colonic luminal short chain fatty acid, which arrests cell growth and induces differentiation in various cell types. We examined the effect of butyrate on the expression of ...WAF1/Cip1, a potent inhibitor of cyclin-dependent kinases, and its relation to growth arrest in a p53-mutated human colon cancer cell line WiDr. Five millimolar butyrate completely inhibited the growth of WiDr and caused G1-phase arrest. WAF1/Cip1 mRNA was rapidly induced within 3 h by treatment with 5.0 mm butyrate, and drastic WAF1/Cip1 protein induction was detected. Using several mutant WAF1/Cip1 promoter fragments, we found that the butyrate-responsive elements are two Sp1 sites at −82 and −69 relative to the transcription start site. We also found that a TATA element at −46 and two overlapping consensus Sp1 sites at −60 and −55 are essential for the basal promoter activity ofWAF1/Cip1. These findings suggest that butyrate arrests the growth of WiDr by activating the WAF1/Cip1 promoter through specific Sp1 sites in a p53-independent fashion.
Treatment of cultured cells with trichostatin A (TSA), a specific histone deacetylase inhibitor, induces the histone hyperacetylation and modulates expression of some mammalian genes. We examined the ...effects of TSA on cell growth arrest, and its relation to expression of the WAF1/Cip1 gene, a potent inhibitor of cyclin-dependent kinases, in a p53-mutated human osteosarcoma cell line MG63. TSA at 500 ng/ml induced growth arrest at both G1 and G2/M phases, and the expressions of the WAF1/Cip1 mRNA and protein. We also examined the changes of acetylated isoforms of histone H4. Dose-response and kinetic analysis suggest a close correlation between the level of histone acetylation and the induction of the WAF1/Cip1 expressions. Using several mutant WAF1/Cip1 promoter fragments, we found that the TSA responsive elements are two Sp1 sites at −82 and −69 relative to the transcription start site. These findings indicate that TSA induces the WAF1/Cip1 promoter through the typical Sp1 sites, in a p53-independent fashion. Furthermore, the Sp1-luc plasmid, containing SV40 promoter-derived three consensus Sp1 binding sites, was markedly activated by TSA, compared to the mutant Sp1-luc plasmid. These results demonstrate that transcriptional activation through the Sp1 sites of the WAF1/Cip1 promoter by TSA coincides with induced hyperacetylation of histone H4.
The mortalin genes, mot-1 and mot-2, are hsp70 family members that were originally cloned from normal and immortal murine cells, respectively. Their proteins differ by only two amino acid residues ...but exhibit different subcellular localizations, arise from two distinct genes, and have contrasting biological activities. We report here that the two proteins also differ in their interactions with the tumor suppressor protein p53. The pancytosolic mot-1 protein in normal cells did not show colocalization with p53; in contrast, nonpancytosolic mot-2 and p53 overlapped significantly in immortal cells. Transfection of mot-2 but not mot-1 resulted in the repression of p53-mediated transactivation in p53-responsive reporter assays. Inactivation of p53 by mot-2 was supported by the down-regulation of p53-responsive genes p21WAF-1 and mdm-2 in mot-2-transfected cells only. Furthermore, NIH 3T3 cells transfected with expression plasmid encoding green fluorescent protein-taggedmot-2 but not mot-1 showed an abrogation of nuclear translocation of wild-type p53. These results demonstrate a novel mechanism of p53 inactivation by mot-2 protein.
By immunoscreening with an antibody raised against a plasma membrane protein, we have cloned a growth suppressor gene, Gros1 and assigned it to short arm of human chromosome 1. Two alternatively ...spliced forms of the gene encoding 84- and 41-kDa (carboxy-terminus truncated) proteins were cloned. The two transcripts, 4.4 and 2.7 kb, were expressed weakly in most of the human tissues, with a high expression of the smaller transcript in placenta, ovary and testis. Normal human fibroblasts in culture showed two transcripts, with a higher level of expression of the 4.4 kb transcript. Transformed cells on the other hand showed predominant expression of the 2.7 kb transcript. Two Gros1 transcripts were also detected in most of the mouse tissues. Stable transfection of the mouse cDNA encoding the 85-kDa protein into NIH3T3 cells resulted in their slow growth and reduced colony-forming efficiency. Stable clones expressing antisense RNA on the other hand exhibited higher colony forming efficiency. While our data implied that Gros1 is a novel growth suppressor gene on human chromosome 1, an independent study has recently characterized its rat-homolog as a leucine proline-enriched novel basement membrane-associated proteoglycan leprecan. We describe here cloning, expression and biological activity analysis implying that this novel proteoglycan is a potential growth suppressor on chromosome 1p31, frequently altered in many malignancies.
We isolated a 33-kDa protein, Pex19p/HK33/HsPXF, as a p19ARF-binding protein in a yeast two-hybrid screen. We demonstrate here that Pex19p interacts with p19ARF in the cell cytoplasm and excludes ...p19ARF from the nucleus, leading to a concurrent inactivation of p53 function. Down-regulation of Pex19p by its antisense expression resulted in increased levels of p19ARF, increased p53 function, and a p53/p21WAF1-mediated senescence-like cell cycle arrest. The data demonstrated a novel mechanism of down-regulation of the p19ARF-p53 pathway.
Classification of high-grade neuroendocrine tumours (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. ...However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification.
We used cDNA microarrays with 40386 elements to analyse the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumours and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analysed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumour samples.
Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83%vs 12% survived for 5 years; p=0·0094. None of the highly proliferative SCLC cell lines subsequently analysed clustered with this good-prognosis group.
Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterised markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuro-endocrine tumours, and provide important insights into their underlying biology.
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