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Novel biomarkers for prostate cancer including noncoding transcripts.

ABSTRACT: Levels of 27 transcripts were investigated as potential novel markers for prostate cancer, including genes encoding plasma membrane proteins (ADAM2, ELOVL5, MARCKSL1, RAMP1, TMEM30A, and TMEM66); secreted proteins (SPON2, TMEM30A, TMEM66, and truncated TMEFF2 (called POP4)); intracellular proteins (CAMK2N1, DHCR24, GLO1, NGFRAP1, PGK1, PSMA7, SBDS, and YWHAQ); and noncoding transcripts (POP1 (100 kb) from mRNA AK000023), POP2 (4 kb from mRNA AL832227), POP3 (50 kb from EST CFI40309), POP5 (intron of NCAM2, accession DO668384), POP6 (intron of FHIT), POP7 (intron of TNFAIP8), POP8 (intron of EFNA5), POP9 (intron of DSTN), POP10 (intron of ADAM2, accession DO668396), POP11 (87kb from EST BG194644), and POP12 (intron of EST BQ226050)). Expression of POP3 was prostate specific, whereas ADAM2, POP1, POP4, POP10, ELOVL5, RAMP1, and SPON2 had limited tissue expression. ELOVL5, MARCKSL1, NGFRAP1, PGK1, POP2, POP5, POP8, PSMA7, RAMP1, and SPON2 were significantly differentially expressed between laser microdissected malignant versus benign clinical samples of prostate tissue. PGK1, POP2, and POP12 correlated to clinical parameters. Levels of CAMK2N1, GLO1, SDBS, and TMEM30A transcripts tended to be increased in primary prostate cancer from patients who later had biochemical failure. Expression of GLO1, DHCR24, NGFRAP1, KLK3, and RAMP1 were significantly decreased in metastatic castration-recurrent disease compared with androgen-dependent primary prostate cancer. These novel potential biomarkers may therefore be useful in the diagnosis/prognosis of prostate cancer.

SUBMITTER: Romanuik TL

PROVIDER: S-EPMC2789638 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Novel biomarkers for prostate cancer including noncoding transcripts.

Romanuik Tammy L TL Ueda Takeshi T Le Nhu N Haile Simon S Yong Theresa M K TM Thomson Thomas T Vessella Robert L RL Sadar Marianne D MD

The American journal of pathology 20091105 6

Levels of 27 transcripts were investigated as potential novel markers for prostate cancer, including genes encoding plasma membrane proteins (ADAM2, ELOVL5, MARCKSL1, RAMP1, TMEM30A, and TMEM66); secreted proteins (SPON2, TMEM30A, TMEM66, and truncated TMEFF2 (called POP4)); intracellular proteins (CAMK2N1, DHCR24, GLO1, NGFRAP1, PGK1, PSMA7, SBDS, and YWHAQ); and noncoding transcripts (POP1 (100 kb) from mRNA AK000023), POP2 (4 kb from mRNA AL832227), POP3 (50 kb from EST CFI40309), POP5 (intro ...[more]

PMID: 19893039

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Project description:BACKGROUND:Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene's directional effect on human cancer. RESULTS:We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. CONCLUSIONS:This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.

Dataset Information

Novel biomarkers for prostate cancer including noncoding transcripts.

Publications

Novel biomarkers for prostate cancer including noncoding transcripts.

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