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Cancer02:18

Cancer

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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What is Cancer?02:12

What is Cancer?

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Cells and tissues must meticulously coordinate their activities for the normal functioning of the human body. Therefore, they exhibit socially responsible behavior - resting, growing, dividing, differentiating, or dying - for the organism’s benefit. Cancer arises when cells divide uncontrollably and invade other tissues or organs.
Although people have known about cancer for centuries, it was only in 1761 that Giovanni Morgagni of Padua performed a detailed autopsy of...
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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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Cancer Therapies02:49

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Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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がん依存性の地図を定義する

Aviad Tsherniak1, Francisca Vazquez2, Phil G Montgomery1

  • 1Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA.

Cell
|July 29, 2017
PubMed
まとめ
この要約は機械生成です。

研究者はゲノムスケールスクリーンを用いて 769の重要な遺伝子を様々な癌細胞系で特定しました 予測モデルは,ほとんどのがん依存症の発現ベースのバイオマーカーを明らかにし,治療目標の優先順位付けを支援しました.

キーワード:
RNAiスクリーン癌による依存症癌のターゲット遺伝的脆弱性についてゲノムバイオマーカー精密医療予測モデリング種子効果shRNA について

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Labeling of Breast Cancer Patient-derived Xenografts with Traceable Reporters for Tumor Growth and Metastasis Studies
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科学分野:

  • 癌の研究
  • ゲノミクス
  • 分子生物学

背景:

  • ヒトの上皮腫瘍には多数の遺伝的変異があり,腫瘍の生存に必要な遺伝子の識別を複雑にしています.
  • 効果的ながん治療法の開発には 癌依存性の体系的な特定が不可欠です

研究 の 目的:

  • 癌細胞の生存に不可欠な遺伝子を 体系的に特定する
  • 分子特性を用いてがん依存性の予測モデルを開発する.
  • 治療目標の優先順位を決めること

主な方法:

  • 人間の癌細胞系における501のゲノムスケール機能喪失スクリーンの分析
  • 標的と非標的のRNAi効果を区別するためのDEMETER分析フレームワークの開発
  • 遺伝子依存性に関する予測モデルを構築するために66,646の分子特性を用いた非線形回帰モデリングの適用.

主要な成果:

  • 癌細胞のサブセットに差異的に必要な769の遺伝子の特定.
  • 426 (55%) の依存関係に対する予測モデルの開発
  • 発現ベースのバイオマーカーが モデルの82%で主要な予測因子であることを発見した.
  • UBB遺伝子ハイパーメチル化とUBC依存性を関連付ける予測モデルの実証

結論:

  • この研究は,大規模な細胞系データセットにおけるがん依存性の包括的な分析を提供します.
  • 発現ベースのバイオマーカーは癌依存性の重要な予測因子であり,治療戦略の洞察を提供します.
  • この発見は 標的型がん治療法の開発を導くための 癌依存性マップの基礎を築きました