Q-omics provides the consensus-scored ASPSCR1 profile across patient tissues and cancer cell-line models. ASPSCR1 expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ASPSCR1 is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, ASPSCR1 protein abundance shows 18,083 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, COAD, and LSCC as cancer lineages where ASPSCR1 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 ASPSCR1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASPSCR1 survival associations across molecular data types. ASPSCR1 RNA expression shows survival associations in the most cancer types (18), followed by mutation status (8) 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 ASPSCR1 RNA expression–survival associations across cancer types. High ASPSCR1 expression shows unfavorable associations in KIRC, ACC, PRAD and ESCA, but favorable associations in HNSC and PAAD. The KIRC 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 KIRC as the clearest survival context for ASPSCR1 RNA expression.
This table summarizes ASPSCR1 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 5. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ASPSCR1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASPSCR1 shows lower tumor expression in KICH and higher tumor expression in COAD, HNSC, LIHC, BLCA and UCEC. The COAD box plot shows higher ASPSCR1 RNA expression in tumor versus normal tissue (log2 FC = +1.057, t-test p < 0.001).
This table shows molecular features associated with ASPSCR1 in patient tissues and cancer cell lines. In patient samples, ASPSCR1 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ASPSCR1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SKIN.
