RESUMO
Cancers of the lung and liver are the top 10 leading causes of cancer death worldwide. Thus, it is essential to identify the genes specifically expressed in these two cancer types to develop new therapeutics. Although many messenger RNA (mRNA) sequencing data related to these cancer cells are available due to the advancement of next-generation sequencing (NGS) technologies, optimized data processing methods need to be developed to identify the novel cancer-specific genes. Here, we conducted an analytical comparison between Bowtie2, a Burrows-Wheeler transform-based alignment tool, and Kallisto, which adopts pseudo alignment based on a transcriptome de Bruijn graph using mRNA sequencing data on normal cells and lung/liver cancer tissues. Before using cancer data, simulated mRNA sequencing reads were generated, and the high Transcripts Per Million (TPM) values were compared. mRNA sequencing reads data on lung/liver cancer cells were also extracted and quantified. While Kallisto could directly give the output in TPM values, Bowtie2 provided the counts. Thus, TPM values were calculated by processing the Sequence Alignment Map (SAM) file in R using package Rsubread and subsequently in python. The analysis of the simulated sequencing data revealed that Kallisto could detect more transcripts and had a higher overlap over Bowtie2. The evaluation of these two data processing methods using the known lung cancer biomarkers concludes that in standard settings without any dedicated quality control, Kallisto is more effective at producing faster and more accurate results than Bowtie2. Such conclusions were also drawn and confirmed with the known biomarkers specific to liver cancer.
Assuntos
Algoritmos , Neoplasias Hepáticas , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Neoplasias Hepáticas/genética , Pulmão , Análise de Sequência de DNA/métodos , SoftwareRESUMO
We have identified an 8-gene signature with significant expression differences between gastric cancer and normal gastric tissues. This 8-gene set can predict the normal and cancer status of gastric tissues with more than 96% accuracy in a totally independent microarray dataset. The 8 genes are composed of down-regulated KLF4, GPX3, SST and LIPF, together with up-regulated SERPINH1, THY1 and INHBA in gastric cancer. To corroborate the differential gene expression pattern, we chose GPX3 and examined its expression pattern in detail. A comparison of GPX3 expression pattern shows a broader down-regulated pattern in multiple types of cancers, including cervical, thyroid, head and neck, lung cancers and melanoma than in healthy controls. An immuno-histostaining analysis in tissue microarrays confirms GPX3 down-regulation in gastric cancer. Mechanism-wise GPX3 down-regulation in gastric cancer is due to promoter hypermethylation. Collectively, these results show a correct identification of 8 genes as gastric cancer biomarkers.
Assuntos
Adenocarcinoma/genética , Biomarcadores Tumorais/genética , Metilação de DNA/genética , Glutationa Peroxidase/genética , Neoplasias Gástricas/genética , Adenocarcinoma/metabolismo , Adulto , Idoso , Cromogranina A/biossíntese , Cromogranina A/genética , Regulação para Baixo , Perfilação da Expressão Gênica , Glutationa Peroxidase/biossíntese , Proteínas de Choque Térmico HSP47/biossíntese , Proteínas de Choque Térmico HSP47/genética , Humanos , Imuno-Histoquímica , Subunidades beta de Inibinas/biossíntese , Subunidades beta de Inibinas/genética , Estimativa de Kaplan-Meier , Fator 4 Semelhante a Kruppel , Fatores de Transcrição Kruppel-Like/biossíntese , Fatores de Transcrição Kruppel-Like/genética , Lipase/biossíntese , Lipase/genética , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Análise de Sequência com Séries de Oligonucleotídeos , Regiões Promotoras Genéticas , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Somatostatina/biossíntese , Somatostatina/genética , Neoplasias Gástricas/metabolismo , Antígenos Thy-1/biossíntese , Antígenos Thy-1/genética , Análise Serial de Tecidos , Regulação para CimaRESUMO
Tautomycetin (TMC) was identified as an immunosuppressor of activated T cells. Inhibition of T cell proliferation with TMC was observed at concentrations 100-fold lower than those needed to achieve maximal inhibition with cyclosporin A (CsA). TMC specifically blocked tyrosine phosphorylation of intracellular signal mediators downstream of Src tyrosine kinases in a T cell-specific manner, leading to apoptosis due to cleavage of Bcl-2, caspase-9, caspase-3, and poly(ADP-ribose) polymerase, but not caspase-1. In TMC-treated rats that received a heterotopic cardiac allograft, the graft survived more than 160 days, comparable to graft survival in allografted rats treated with CsA. Thus, TMC, whose mechanism of action is different from that of CsA or FK506, can be used as a potent T cell-specific immunosuppressor.