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"The development and progression of cancer and the experimental reversal of tumorigenicity are companied by complex changes in the pattern of gene expression.DNA micro-array technology is used to profile complex disease and discover novel disease-related genes. This technique has been successfully used to investigate gene expression in processes as complex as inflammatory disease, tumor suppression and to identify heat shock in human T cell.DNA micro-arrays or DNA-chip technology allows expression monitoring of hundreds and thousands of genes simultaneously and provide a format for identifying genes as well as changes in their activity. The DNA micro-array helps us study genome-wide expression patterns in complex biological systems. These tools have shown great promise in finding the meaning of complex diseases such as cancer.There is some interest in the potential application of DNA micro-array analysis for gene expression profiling in human cancers. Micro-arrays of DNA provide a powerful tool for studying the development and progression of cancer phenomena. It might be useful for tumor classification, for the elucidation of regulatory networks that are disturbed in tumor cells and for the identification of genes that might be of use for diagnostic purposes or as therapeutic targets."

"Providing an interface between dry-bench bioinformaticians and wet-lab biologists, DNA Methylation Microarrays: Experimental Design and Statistical Analysis presents the statistical methods and tools to analyze high-throughput epigenomic data, in particular, DNA methylation microarray data"

"Integrasi data gene expression dan DNA copy number berbasis kernel digunakan untuk menganalisis pola gen pada penyakit kanker payudara cell line. Clustering pada data integrasi dilakukan tanpa adanya informasi jumlah k cluster, teknik ini disebut fully unsupervised clustering. Pada penelitian ini, intelligent kernel K-Means dikembangkan dengan menggabungkan teknik intelligent K-Means dan kernel K-Means. Berdasarkan hasil eksperimen, nilai pada kernel RBF mempengaruhi jumlah cluster yang ditemukan. Hasil clustering dievaluasi menggunakan nilai R, global silhouette, indeks Davies-Bouldien, akurasi LS-SVM dan visualisasi. Hasil esperimen terbaik yaitu 3 cluster yang memperoleh akurasi LS-SVM sebesar 97.3% dengan standar deviasi 0.2%.

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In this thesis, kernel based data integration of gene expression and DNA copy number would be utilized to analyze pattern of genes in breast cancer cell line. The cluster analysis on the integrated data will be conducted with has no prior information with regards the number of k clusters which is called fully unsupervised clustering technique. In this work, intelligent kernel K-Means is proposed by combining intelligent K-Means and kernel K-Means. From the experiments, the value of of Radial Basis Function (RBF) has important role for finding the optimal of number of cluster. The clusters those to be found will be evaluated based on global silhouette, Davies-Bouldien Index, LS-SVM accuracy and visualization. The experiment result show that three clusters are successfully to be found. Those clusters produce average accuracy of LS-SVM around 97.3 % with standard deviation 0.2 %."

"Penelitian bioinformatika sering diterapkan untuk mempelajari penyakit dalam tubuh manusia. Penelitian yang sampai saat ini masih aktif dilakukan ialah penelitian terhadap pasien penderita kanker. Tujuan dari berbagai penelitian ini yaitu untuk menemukan pengobatan terbaik bagi pasien penderita kanker. Salah satu pengobatan yang baru ini muncul dikenal sebagai imunoterapi. Imunoterapi memungkinkan sel-sel imun tubuh kita sendiri digunakan untuk melawan sel-sel kanker. Instrumen utama dalam penelitian terhadap efektifitas imunoterapi juga kasus bioinformatika lainnya ialah data ekspresi gen. Namun, pada data ekspresi gen seringkali ditemukan nilai yang hilang atau missing values yang biasanya disebabkan oleh kerusakan gambar atau kesalahan dalam proses hibridisasi. Keberadaan missing values pada data ekspresi gen dapat menyebabkan kesulitan pada analisis lebih lanjut, di mana banyak analisis ekspresi gen memerlukan data yang lengkap seperti klasifikasi dan pengelompokan. Oleh karena itu, perlu dilakukan imputasi terhadap missing values agar analisis yang dilakukan dapat lebih akurat. Pada penelitian ini dilakukan imputasi menggunakan metode Bi-BPCA. Bi-BPCA merupakan metode imputasi dengan mengombinasikan analisis biclustering dan imputasi BPCA. Metode Bi-BPCA diterapkan pada data ekspresi gen di sekitar kanker setelah dilakukan imunoterapi. Setelah itu, performa dari metode Bi-BPCA dilihat dengan membandingkan hasil imputasi metode Bi-BPCA dengan metode imputasi lainnya diantaranya imputasi menggunakan rata-rata baris, rata-rata kolom, dan metode imputasi BPCA melalui nilai NRMSE. Selain itu, koefisien korelasi Pearson digunakan untuk menghitung korelasi antara nilai hasil imputasi metode Bi-BPCA dengan nilai aslinya. Berdasarkan penelitian ini metode Bi-BPCA menghasilkan NRMSE kurang dari 0.6 untuk missing rate 1-30%, lebih rendah dibandingkan NRMSE dari metode imputasi lainnya. Kemudian, metode Bi-BPCA menghasilkan nilai koefisien korelasi Pearson mayoritas di atas 0.9 mendekati 1. Hasil ini menunjukkan bahwa metode Bi-BPCA menghasilkan nilai imputasi yang lebih baik untuk menggantikan missing values dibandingkan dengan metode imputasi BPCA, rata-rata kolom, dan rata-rata baris.

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Bioinformatics research is often applied to study diseases in the human body. Research that is still actively being carried out is research on cancer patients. The aim of those studies is to find the best treatment for cancer patients. One treatment that has recently emerged is known as immunotherapy. Immunotherapy allows our body's own immune cells to be used to fight cancer cells. The main instrument in research on the effectiveness of immunotherapy as well as other cases of bioinformatics is gene expression data.. However, in gene expression data, it is often found missing values which are usually caused by image defects and errors in the hybridization process. The existence of missing values in gene expression data can cause difficulties in further analysis, where many analysis of gene expression requires complete data such as classification and clustering. Therefore, it is necessary to impute the missing values so that the analysis can be carried out more accurately. In this study, imputation was carried out using the Bi-BPCA method. Bi-BPCA is an imputation method by combining biclustering analysis and BPCA imputation. The Bi-BPCA method was applied to gene expression data around cancer after immunotherapy. After that, the performance of the Bi-BPCA method was seen by comparing the imputation results of the Bi-BPCA method with other imputation methods including imputation using row averages, column averages, and the BPCA imputation method through the NRMSE value. In addition, the Pearson correlation coefficient was used to calculate the correlation between the imputed value of the Bi-BPCA method and the original value. Based on this study, the Bi-BPCA method produces NRMSE values less than 0.6 for missing rates 1 to 30 percent, which is lower than NRMSE from other imputation methods. In addition, the Bi-BPCA method produces in a majority Pearson correlation coefficient above 0.9. These results indicate that the Bi-BPCA method produces better imputation values to replace the missing values."

"Kanker payudara merupakan salah satu jenis kanker dengan kematian terbanyak kedua di dunia. Penyebab utama kematian penderita kanker payudara adalah metastasis sel kanker ke jaringan lain. Salah satu penanganan kanker payudara adalah pemberian pengobatan kemoterapi. Namun kemoterapi seringkali menimbulkan efek samping pada pasien. Salah satu jenis kemoterapi umum, Doxorubicin mampu meningkatkan resiko toksisitas jantung. Karakteristik kultur eksplan yang dapat mempertahankan kondisi sel secara in vivo dapat digunakan untuk memprediksi respons kemoterapi. Namun demikian, penggunaan kultur eksplan untuk memprediksi respon kemoterapi belum banyak diterapkan. Gen MYCN memiliki peran dalam mendorong keganasan sel kanker dan banyak terekspresi pada kanker payudara dengan prognosis buruk, sehingga dapat digunakan sebagai biomarker untuk memastikan respon antara jaringan asal dan kultur eksplan relatif sama. Oleh karena itu penelitian ini bertujuan untuk menganalisis ekspresi gen MYCN pada jaringan asal dan kultur eksplan serta menganalisis tingkat ekspresi gen MYCN pada kultur eksplan terhadap treatment Doxorubicin dengan metode one-step semi-kuantitatif Reverse Transcriptation - Polymerase Chain Reaction (RT-PCR). Perlakuan treatment dilakukan untuk mendukung kemampuan kultur eksplan dalam mempertahankan in vivo. Hasil yang diperoleh menunjukkan ekspresi relatif gen MYCN pada jaringan asal dan kultur eksplan relatif sama serta adanya ekspresi gen MYCN setelah pemberian treatment pada kultur eksplan. Hal tersebut menandakan kultur eksplan dapat mempertahankan ekspresi dari jaringan asalnya. Selain itu, adanya ekspresi gen MYCN setelah pemberian agen kemoterapi mengkonfirmasi bahwa kultur eksplan memiliki respons yang relatif sama setelah pemberian treatment sehingga dapat digunakan untuk memprediksi respon kemoterapi.

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Breast cancer is one type of cancer with the second most deaths in the world. The main cause of death for breast cancer patients is the metastasis of cancer cells to other tissues. One of the treatments for breast cancer is chemotherapy treatment. However, chemotherapy often causes side effects in patients. One of the common types of chemotherapy, Doxorubicin can increase the risk of cardiac toxicity. Characteristics of explant cultures that can maintain cell conditions in vivo can be used predicting chemotherapy response. However, the use of explant cultures to predict chemotherapy response has not been widely applied. The MYCN gene has a role in promoting cancer cell malignancy and is widely expressed in breast cancer with a poor prognosis, so it can be used as a biomarker to ensure that the response between the tissue of origin and explant cultures is relatively similar. Therefore, this study aims to analyze MYCN gene expression in the original tissue and explant culture and to analyze the expression level of the MYCN gene in explant culture against Doxorubicin treatment using a one-step semi-quantitative Reverse Transcriptation - Polymerase Chain Reaction (RT-PCR) method. The treatment was carried out to support the ability of the explant culture to maintain in vivo. The results obtained showed that the relative expression of the MYCN gene in the original tissue and the explant culture was relatively the same as well as the expression of the MYCN gene after the treatment was given to the explant culture. This indicates that the explant culture can maintain the expression of the original tissue. In addition, the presence of MYCN gene expression after administration of chemotherapeutic agents confirmed that explant cultures had relatively the same response after treatment, so that they could be used to predict chemotherapy responses."

"Kanker paru merupakan salah satu jenis kanker mematikan dengan terjadinya kasus paling banyak di dunia. Mutasi gen yang terjadi pada sel organ paru menjadi penyebab utama terjadinya kanker paru. Salah satu gen yang berpengaruh terhadap pembelahan sel kanker, yaitu ferritin mitokondria (FTMT). Mekanisme yang diatur oleh gen FTMT yaitu dengan memodulasi metabolisme zat besi (Fe2+) didalam mitokondria yang diinduksi oleh adanya stres oksidatif. Mekanisme yang diatur oleh gen FTMT dengan jumlah ekspresi yang tinggi akibat adanya stres oksidatif berupa H2O2 didalam sel kanker. Senyawa stres oksidatif berupa H2O2 merupakan senyawa toksik yang dapat menghasilkan Reactive Oxygen Spesies (ROS) dan berperan penting dalam proses pengaturan sistem fisiologis dalam sel. Reactive Oxygen Spesies (ROS) yang dihasilkan dengan jumlah tinggi akan mengarah ke mekanisme kematian sel (ferroptosis). Penelitian ini bertujuan untuk mengetahui ekspresi gen FTMT dan viabilitas sel pada cell line A549 (non-small cell lung carcinoma) yang diinduksi oleh senyawa H2O2 sebagai stres oksidatif dengan perlakuan berbagai konsentrasi, yaitu 50 μM, 100 μM, 150 μM, 200 μM, dan 300 μM dengan menggunakan metode qRT-PCR. Hasil penelitian ini menunjukkan peningkatan ekspresi gen FTMT dan penurunan viabilitas sel secara signifikan pada perlakuan H2O2 dengan rentang konsentrasi 50 μM sampai 100 μM. Dengan demikian, perlakuan stres oksidatif H2O2 mempunyai peran penting dalam meregulasi gen FTMT yang berkaitan dengan morfologi dan viabilitas sel A549 kanker paru.

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Lung cancer is a type of deadly cancer with the most cases occurring in the world. Gene mutations that occur in lung organ cells are the main cause of lung cancer. One of the genes that influences cancer cell division is mitochondrial ferritin (FTMT). The mechanism regulated by the FTMT gene is by modulating iron (Fe2+) metabolism in mitochondria which is induced by oxidative stress. The mechanism regulated by the FTMT gene with high levels of expression is due to oxidative stress in the form of H2O2 in cancer cells. Oxidative stress compounds in the form of H2O2 are toxic compounds that can produce Reactive Oxygen Species (ROS) and play an important role in the process of regulating physiological systems in cells. Reactive Oxygen Species (ROS) produced in high amounts will lead to a cell death mechanism (ferroptosis). This study aims to determine the expression of the FTMT gene and cell viability in the A549 (non-small cell lung carcinoma) cell line which was induced by H2O2 compounds as oxidative stress with various concentrations of treatment, namely 50 μM, 100 μM, 150 μM, 200 μM, and 300 μM using the qRT-PCR method. The results of this study showed an increase in FTMT gene expression and a significant decrease in cell viability in H2O2 treatment with a concentration range of 50 μM to 100 μM. Thus, H2O2 oxidative stress treatment has an important role in regulating the FTMT gene which is related to the morphology and viability of lung cancer A549 cells."

"Background: According to WHO, breast cancer has the highest incidence rate among women. Breast cancer is caused by the uncontrolled growth of abnormal cells that form in breast tissue, triggered by the presence of cancer stem cells. The invasive properties of breast stem cells are closely related to the pluripotency of these cells. The pluripotency of a cell is closely related to the genes expressed. In this study, c-Myc gene expression was observed to determine the level of pluripotency of breast cancer stem cell fraction samples separated using the Magnetic Activated Cell Sorting (MACS) technique. Method: mRNA was obtained from 11 breast cancer stem cell samples which were fractionated using MACS. The expression of c-Myc in these cell fractions was analyzed using one step real time RT-PCR with SYBR Green ( Bioneer®) and electrophoresis. Results: Based on the experimental results, high level expression of c-Myc was present in the CD24-/44- cell fraction, while low level expression of the c-Myc gene was found in the CD24-/44+ cell fraction. Conclusions: The c-Myc gene is expressed in all breast cancer stem cell fractions. Looking at the c-Myc gene expression, higher levels of pluripotency can be found in the CD24-/44- cell fraction compared to CD24-/44+. "