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Abstrak :
Insidensi kanker payudara terus meningkat secara global setiap tahun. Di negara berpendapatan menengah ke bawah, meningkatnya insidensi ini diikuti dengan meningkatnya angka kematian akibat kanker payudara yang disebabkan oleh keterlambatan diagnosis yang sering terjadi, sehingga pengobatan dan perawatan kanker payudara tidak lagi efektif dilakukan. Pada penelitian ini dilakukan analisis metode peningkatan deteksi dini terencana kanker payudara yang telah dilakukan di beberapa negara berpendapatan menengah ke bawah dengan menggunakan systematic review. Systematic review dilakukan dengan melakukan identifikasi literatur dari Google scholar, ProQuest, ScienceDirect, Scopus, dan Pubmed dengan menggunakan kata kunci improve, early detection, screening, breast cancer, low middle-income countries, dan LMIC, lalu dilakukan pencarian lanjutan dengan teknik snowball dari literatur yang sudah didapatkan. Kriteria inklusi pada pencarian literatur adalah artikel full text berbahasa Inggris yang diterbitkan pada tahun 2010 – Juni tahun 2020 dan memiliki lokasi studi di negara berpendapatan menengah ke bawah. Sebelas artikel didapatkan dari pencarian dan proses seleksi menggunakan diagram alir PRISMA. Ditemukan 5 jenis program dalam upaya peningkatan deteksi dini kanker payudara di negara berpendapatan menengah ke bawah, yaitu program skrining berbasis populasi, program skrining oportunistik, program peningkatan pengetahuan dan kesadaran, program pengalihan tugas, dan pelaksanaan program nasional deteksi dini kanker payudara. Program-program tersebut diketahui berhasil meningkatkan deteksi dini kanker payudara pada latar negara berpendapatan menengah ke bawah. Program peningkatan pengetahuan dan kesadaran, dan program pengalihan tugas dinilai sebagai program yang paling sesuai untuk dilaksanakan di negara berpendapatan menengah ke bawah. Meski begitu, perlu dilakukan pilot studi dan evaluasi biaya untuk melihat keefektifan program deteksi dini ini.

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Incidence of breast cancer keeps increasing globally every year, and in low-and middle-income countries it followed by increased mortality rate. This phenomenon could be associated with late-stage diagnosis that made treatments no longer effective. This study aimed to analyse methods to increase early detection for breast cancer that has been done in several low-and middle-income countries by using systematic review. Systematic review was carried out by identifying literatures from online databases such as Google scholar, ProQuest, ScienceDirect, Scopus, and Pubmed using “improve”, “early detection”, “screening”, “breast cancer”, “low middle-income countries", and “LMIC” as keywords, and followed by manually searching literatures using snowball technique from the literatures that had been identified earlier. Inclusion criterias in this study were English full text that were published between 2010 – June 2020 with low-and middle-income countries setting. Eleven articles were obtained using PRISMA flow diagram and 5 types of programs were found to increase early detection for breast cancer in low-and middle-income countries; population-based screening program, oportunistic screening program, program to improve knowledge and awareness about breast cancer and early detection, task shifting program, and implementation of national breast cancer early detection program. These programs were known to be able to improve early detection in their respective study location. Improving knowledge and awareness, and task shifting program were the most suitable program to be executed in low-and middle-income countries setting. These countries shall do some pilot studies and cost evaluation to identify and analyse the effectivity of these programs before implementing it widely.

Abstrak :
Kanker merupakan kelompok penyakit yang ditandai dengan pertumbuhan dan penyebaran sel-sel abnormal yang tidak terkendali. Jika penyebaran sel tersebut tidak terkendali, hal ini dapat menyebabkan kematian. Berdasarkan American Cancer Society, pendeteksian dini terhadap sel kanker dapat meningkatkan angka harapan hidup seorang pasien lebih dari 97 . Banyak penelitian yang telah meneliti mengenai klasifikasi kanker menggunakan microarray data. Microarray data terdiri dari ribuan fitur gen namun hanya memiliki puluhan atau ratusan sampel. Hal tersebut dapat menurunkan akurasi klasifikasi sehingga perlu dilakukannya pemilihan fitur sebelum proses klasifikasi. Pada penelitian ini dilakukan dua tahap pemilihan fitur. Pertama, support vector machine recursive feature elimination SVM-RFE digunakan untuk prefilter gen. Kedua, hasil pemilihan fitur SVM-RFE diseleksi kembali dengan menggunakan artificial bee colony ABC yang merupakan algoritma optimisasi berdasarkan perilaku lebah madu. Penelitian ini menggunakan dua dataset, yaitu data kanker paru-paru Michigan dan Ontario dari Kent Ridge Biomedical Dataset. Hasil percobaan dengan menggunakan SVM-RFE dan ABC menunjukkan nilai akurasi klasifikasi yang lebih tinggi daripada tanpa pemilihan fitur, SVM-RFE, dan ABC, yaitu 98 untuk data kanker paru-paru Michigan dengan menggunakan 100 fitur dan 97 untuk data kanker paru-paru Ontario dengan menggunakan 70 fitur. ......Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells. If the spread is not controlled, it can result in death. Based on American Cancer Society, early detection of cancerous cells can increase survival rates for patients by more than 97 . Many study showed new aspect of cancer classification based microarray data. Microarray data are composed of many thousands of features genes and from tens to hundreds of instances. It can decrease classification accuracy so feature selection is needed before the classification process In this paper, we propose two stages feature selection. First, support vector machine recursive feature elimination recursive feature elimination SVM RFE is used to prefilter the genes. Second, the SVM RFE features selection result is selected again using Artificial Bee Colony ABC which is an optimization algorithm based on a particular intelligent behavior of honeybee swarms. This research conducted experiments on Ontario and Michigan Lung Cancer Data from Kent Ridge Biomedical Dataset. Experiment results demonstrate that this approach provides a higher classification accuracy rate than without feature selection, SVM RFE, and ABC, 98 for Michigan lung cancer dataset with using 100 features and 97 for Ontario lung cancer dataset with using 70 features.

Abstrak :
In recent years, cancer stem cells have been recognized as important component in carcinogenesis and they seem to form the basis of many (if not all) tumor types. Cancer stem cells or "cancer cell like stem cells" have been isolated from various cancers of different origin (blood, breast, brain, skin, head and neck, thyroid, cervix, lung, retina, colon, pancreas and so on). Cancer stem cells - rare cells with indefinite proliferative potential that drive the formation and growth of tumours- seem to show intriguing relationships with physiological stem cells. Specifically, these cancer cells show significant similarities in the mechanisms that regulate self-renewal of normal stem cells. Moreover, tumour cells might directly arise from normal stem cells. Further, the cellular biology of cancer stem cells show a lot of similarities with normal stem cells.

Abstrak :
Abnormalitas metabolisme glukosa yang terjadi pada pasien diabetes mengambarkan peningkatan risiko pada perkembangan dan prognosis kanker tertentu. Metformin yang merupakan terapi lini pertama untuk pasien diabetes melitus tipe 2 telah dikaitkan dengan risiko berkurangnya berbagai sel kanker. Penelitian ini membandingkan dan menganalisis efektivitas pengobatan metformin tunggal/kombinasi dan non-metformin terhadap nilai antibodi anti-p53 dan hubungannya dengan nilai HbA1c pada pasien T2DM yang memiliki faktor risiko kanker. Jenis penelitian yang dilakukan merupakan penelitian observasional dengan menggunakan metode cross-sectional dengan jumlah sampel 32 orang yang diambil di Puskesmas Pasar Minggu, Puskesmas Cimanggis, RSUD Depok, dan pasien volunteer di kabupaten Tangerang dengan teknik total sampling. Nilai rerata antibodi anti-p53 diukur menggunakan elisa kit MESACUP anti-p53 Test, sedangkan HbA1c diukur di lab klinik terakreditasi dengan metode ion exchange HPLC. Nilai antibodi anti-p53, yaitu 0,17 ± 0,07 pada kelompok metformin tunggal/kombinasi dan 0,25 ± 0,12 pada kelompok non-metformin. Namun, nilai antibodi anti-p53 pada kedua kelompok tidak memiliki perbedaan yang bermakna (p = 0,970). Analisis korelasi antara nilai HbA1c dengan nilai antibodi anti-p53 pada kelompok non-metformin menghasilkan hubungan negatif yang kuat dan bermakna (r = -0,709; p = 0,003). Sedangkan pada kelompok metformin tunggal/atau kombinasi tidak ditemukan hubungan yang bermakna (r = -0,056; p = 0,830). ...... Abnormalities of glucose metabolism that occur in diabetic patients describe an increased risk in the development and prognosis of certain cancers. Metformin is a first-line therapy for patients with type 2 diabetes melitus has been associated with a reduced risk of various cancer cells. This study compares and analyzes the effectiveness of treatment in group of metformin or combination and non-metformin towards anti-p53 antibody ​​and their relation to HbA1c in T2DM patients who have risk factors for cancer. Type of research is an observational study using cross-sectional method with a total sample is 32 people in Puskesmas Pasar Minggu, Puskesmas Cimanggis, RSUD Depok, and volunteers patient in Tangerang district using total sampling technique. Anti-p53 antibody was measured using elisa kit MESACUP anti-p53 Test whereas HbA1c was measured in accredited clinical lab by ion exchange HPLC method. The average of anti-p53 antibody is 0,17 ± 0,07 in metformin single dose/combinations group and 0,25 ± 0,12 in non-metformin group. Moreover, there was no significance difference between the group of metformin or combination and non-metformin (p = 0,970). On the other hand, there was a strongly correlation between HbA1c values and anti-p53 antibody in group of non-metformin (r = -0,709; p = 0,003) but none in the group of single dose or combinations group (r = -0,056; p = 0,830).

Abstrak :
Diagnostic, prognostic and therapeutic value of gene signatures provides readers a useful and comprehensive resource about the range of applications of microarray technology in oncological diseases. Topics covered include gene signatures and soft tissue sarcomas, prognostic relevance of breast cancer signatures, gene expression profiling of colorectal cancer and liver metastasis, gene signatures in GISTs, CNVs and gene expression profiles in pancreatic cancer, and gene signatures in head/neck, lung and gastric tumors.