Q-omics provides the consensus-scored TESK1 profile across patient tissues and cancer cell-line models. TESK1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, TESK1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, TESK1 RNA expression shows 19,249 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, HNSC, and ACC as cancer lineages where TESK1 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 TESK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TESK1 survival associations across molecular data types. TESK1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TESK1 RNA expression–survival associations across cancer types. High TESK1 expression shows unfavorable associations in ACC, MESO and LIHC, but favorable associations in UVM, SCLC and OV. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify UVM as the clearest survival context for TESK1 RNA expression.
This table summarizes TESK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for TESK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TESK1 shows lower tumor expression in BRCA and higher tumor expression in HNSC, KIRC, LIHC, KIRP and CHOL. The HNSC box plot shows higher TESK1 RNA expression in tumor versus normal tissue (log2 FC = +1.124, t-test p < 0.001).
This table shows molecular features associated with TESK1 in patient tissues and cancer cell lines. In patient samples, TESK1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, TESK1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
