Medical Interventions 2.1.5 review

  • Gene Expression Basics

    • Gene expression is indicative of whether a gene is turned up or down.

    • A gene expressed equally in cancer and normal cells yields a value of zero.

    • Color coding for gene expression:

      • Green indicates super negative expression

      • Red indicates super positive expression.

      • Yellow represents zero (equal expression).

  • Interpreting Gene Expression Values

    • Example values and their meanings:

    • Example: A value of 4 means turned up significantly (super induced in cancer).

    • Value of 0.67 indicates it is slightly up in cancer.

    • Value of 1 suggests more induced than lower values but less than a higher value.

    • Negative values (e.g., -1, -0.5, -2) indicate down regulation.

    • Values close to zero suggest genes are behaving identically (trending towards zero).

  • Identifying Trends in Gene Expression

    • Assess overall trend by comparing expression levels of different genes across samples.

    • Positive trend means genes are turned up.

    • Negative trend means genes are turned down.

    • Zero indicates equal expression or identical results across samples.

  • Pearson Correlation Coefficient (PCC)

    • PCC is used to compare gene expression across different patients.

    • Higher positive values indicate similar gene behavior among patients.

    • Aim for values close to 1 for identical behavior; 0 indicates no relationship; negative values indicate opposite behavior.

    • Example: If patient A has a PCC of 0.70 with Mike, they react similarly, as opposed to someone with -0.50 who reacts oppositely.

  • Application in Cancer Treatment

    • Treatment decisions (like chemotherapy) can be guided by understanding gene expression in relation to patient genetics.

    • Different patients may have various reactions to chemotherapy based on unique genetic expressions (pharmacogenetics).

    • For instance, if Mike can’t metabolize a certain compound, find a suitable alternative for him.

  • Choosing Treatment Based on Patient Comparison

    • Compare Mike's gene expression with other patients based on PCC values to find the best treatment.

    • Patient with a matching expression trend would be ideal for determining effective treatments.

  • Overall Strategies for Exam Preparation

    • Expect questions on gene expression values: determine if they are turned up or down.

    • Analyze graphs to gauge positive or negative trends in gene expression across samples.

    • Understand hierarchical clustering as a means to visualize relationships among patients based on gene expression.

    • Example of analogous concepts: Cladograms in biology assess evolutionary relationships, similarly interpreting gene connections.

  • Final Advice for the Exam

    • Stay confident and practice interpreting both numerical data and graphs, applying what you know about gene expression and patient correlations effectively in this context.

    • Remember to deepen conceptual understanding behind gene expression, its impacts on treatment, and the mathematical models used for analysis.