Q-omics provides the consensus-scored PACC1 profile across patient tissues and cancer cell-line models. PACC1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PACC1 is differentially expressed in 18, with the highest sampling consensus in HNSC. Additionally, PACC1 RNA expression shows 21,717 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, HNSC, and GBM as cancer lineages where PACC1 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 PACC1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PACC1 survival associations across molecular data types. PACC1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) 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 PACC1 RNA expression–survival associations across cancer types. High PACC1 expression shows unfavorable associations in UVM, MESO, ACC, KIRP, LIHC and BLCA. The UVM 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 UVM as the clearest survival context for PACC1 RNA expression.
This table summarizes PACC1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 18, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PACC1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PACC1 shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, COAD, LIHC and STAD. The HNSC box plot shows higher PACC1 RNA expression in tumor versus normal tissue (log2 FC = +1.643, t-test p < 0.001).
This table shows molecular features associated with PACC1 in patient tissues and cancer cell lines. In patient samples, PACC1 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, PACC1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in BONE and LUNG_SCLC.
