Q-omics provides the consensus-scored CYTH2 profile across patient tissues and cancer cell-line models. CYTH2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, CYTH2 is differentially expressed in 13, with the highest sampling consensus in LIHC. Additionally, CYTH2 RNA expression shows 19,477 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where CYTH2 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 CYTH2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYTH2 survival associations across molecular data types. CYTH2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) 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 CYTH2 RNA expression–survival associations across cancer types. High CYTH2 expression shows unfavorable associations in ACC, MESO, LIHC, LUSC and LGG, but favorable associations in BLCA. The ACC 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 ACC as the clearest survival context for CYTH2 RNA expression.
This table summarizes CYTH2 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 5. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CYTH2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYTH2 shows lower tumor expression in KICH and higher tumor expression in LIHC, COAD, UCEC, STAD and CHOL. The LIHC box plot shows higher CYTH2 RNA expression in tumor versus normal tissue (log2 FC = +1.662, t-test p < 0.001).
This table shows molecular features associated with CYTH2 in patient tissues and cancer cell lines. In patient samples, CYTH2 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, CYTH2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
