Q-omics provides the consensus-scored AMY1C profile across patient tissues and cancer cell-line models. AMY1C expression is associated with patient survival in 9 of 34 cancer types, with the highest sampling consensus in UCS. Additionally, AMY1C RNA expression shows 6,975 significant gene co-expression associations, with the highest sampling consensus in STAD. Together, these results highlight UCS, and STAD as cancer lineages where AMY1C shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for AMY1C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AMY1C survival associations across molecular data types. AMY1C RNA expression shows survival associations in the most cancer types (9), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AMY1C RNA expression–survival associations across cancer types. High AMY1C expression shows unfavorable associations in UCS, LUSC, KIRC and PAAD, but favorable associations in LUAD and OV. The UCS Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .009). Together, the overview and detailed table identify UCS as the clearest survival context for AMY1C RNA expression.
This table shows molecular features associated with AMY1C in patient tissues and cancer cell lines. In patient samples, AMY1C shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, AMY1C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in SKIN.
