Q-omics provides the consensus-scored PACS2 profile across patient tissues and cancer cell-line models. PACS2 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, PACS2 is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, PACS2 protein abundance shows 25,675 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRP, and GBM as cancer lineages where PACS2 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 PACS2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PACS2 survival associations across molecular data types. PACS2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) 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 PACS2 RNA expression–survival associations across cancer types. High PACS2 expression shows unfavorable associations in ACC, LIHC and CESC, but favorable associations in UCS, KIRP and THYM. 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 PACS2 RNA expression.
This table summarizes PACS2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PACS2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PACS2 shows lower tumor expression in BRCA and higher tumor expression in KIRP, LIHC, COAD, HNSC and CHOL. The KIRP box plot shows higher PACS2 RNA expression in tumor versus normal tissue (log2 FC = +0.867, t-test p < 0.001).
This table shows molecular features associated with PACS2 in patient tissues and cancer cell lines. In patient samples, PACS2 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, PACS2 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 BONE and BLOOD_Leukemia.
