Q-omics provides the consensus-scored ATP2C1 profile across patient tissues and cancer cell-line models. ATP2C1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ATP2C1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, ATP2C1 protein abundance shows 30,235 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, HNSC, and GBM as cancer lineages where ATP2C1 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 ATP2C1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP2C1 survival associations across molecular data types. ATP2C1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (8) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP2C1 RNA expression–survival associations across cancer types. High ATP2C1 expression shows unfavorable associations in MESO, PAAD and LIHC, but favorable associations in KIRC, HNSC and LUSC. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify MESO as the clearest survival context for ATP2C1 RNA expression.
This table summarizes ATP2C1 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 11. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ATP2C1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP2C1 shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, LIHC, COAD and STAD. The HNSC box plot shows higher ATP2C1 RNA expression in tumor versus normal tissue (log2 FC = +1.327, t-test p < 0.001).
This table shows molecular features associated with ATP2C1 in patient tissues and cancer cell lines. In patient samples, ATP2C1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP2C1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
