Q-omics provides the consensus-scored SDK1 profile across patient tissues and cancer cell-line models. SDK1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, SDK1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, SDK1 RNA expression shows 17,682 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight LGG, HNSC, and TGCT as cancer lineages where SDK1 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 SDK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SDK1 survival associations across molecular data types. SDK1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (14) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SDK1 RNA expression–survival associations across cancer types. High SDK1 expression shows unfavorable associations in LGG and KICH, but favorable associations in KIRC, PAAD, ACC and HNSC. The LGG Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify LGG as the clearest survival context for SDK1 RNA expression.
This table summarizes SDK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SDK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SDK1 shows lower tumor expression in KICH and COAD and higher tumor expression in HNSC, KIRP, THCA and LUSC. The HNSC box plot shows higher SDK1 RNA expression in tumor versus normal tissue (log2 FC = +1.546, t-test p < 0.001).
This table shows molecular features associated with SDK1 in patient tissues and cancer cell lines. In patient samples, SDK1 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, SDK1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LUNG_SCLC.
