Q-omics provides the consensus-scored PACSIN1 profile across patient tissues and cancer cell-line models. PACSIN1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PACSIN1 is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, PACSIN1 protein abundance shows 24,670 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight HNSC, COAD, and GBM as cancer lineages where PACSIN1 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 PACSIN1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PACSIN1 survival associations across molecular data types. PACSIN1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PACSIN1 RNA expression–survival associations across cancer types. High PACSIN1 expression shows unfavorable associations in UVM and MESO, but favorable associations in HNSC, SKCM, PAAD and LUAD. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for PACSIN1 RNA expression.
This table summarizes PACSIN1 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 6. The strongest signals are observed in COAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PACSIN1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PACSIN1 shows lower tumor expression in COAD and higher tumor expression in LUAD, UCEC, BRCA, LUSC and THCA. The COAD box plot shows higher PACSIN1 RNA expression in normal versus tumor tissue (log2 FC = −0.567, t-test p < 0.001).
This table shows molecular features associated with PACSIN1 in patient tissues and cancer cell lines. In patient samples, PACSIN1 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, PACSIN1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
