phosphofurin acidic cluster sorting protein 1Genealiases: MRD17 · SHMS
Q-omics provides the consensus-scored PACS1 profile across patient tissues and cancer cell-line models. PACS1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, PACS1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, PACS1 protein abundance shows 26,918 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, HNSC, and GBM as cancer lineages where PACS1 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 PACS1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PACS1 survival associations across molecular data types. PACS1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) 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 PACS1 RNA expression–survival associations across cancer types. High PACS1 expression shows unfavorable associations in MESO, LUAD, LGG and ACC, but favorable associations in ESCA and UVM. The MESO 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 MESO as the clearest survival context for PACS1 RNA expression.
This table summarizes PACS1 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 HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PACS1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PACS1 shows higher tumor expression in HNSC, LIHC, KICH, LUAD, LUSC and CHOL. The HNSC box plot shows higher PACS1 RNA expression in tumor versus normal tissue (log2 FC = +0.891, t-test p < 0.001).
This table shows molecular features associated with PACS1 in patient tissues and cancer cell lines. In patient samples, PACS1 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, PACS1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
