Q-omics provides the consensus-scored SCML1 profile across patient tissues and cancer cell-line models. SCML1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, SCML1 is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, SCML1 RNA expression shows 19,051 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRP, COAD, and ACC as cancer lineages where SCML1 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 SCML1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCML1 survival associations across molecular data types. SCML1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) 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 SCML1 RNA expression–survival associations across cancer types. High SCML1 expression shows unfavorable associations in KIRP, ACC, LGG, LIHC and KICH, but favorable associations in SKCM. The KIRP 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 KIRP as the clearest survival context for SCML1 RNA expression.
This table summarizes SCML1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 3. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SCML1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCML1 shows lower tumor expression in BRCA and higher tumor expression in COAD, READ, LIHC, HNSC and STAD. The COAD box plot shows higher SCML1 RNA expression in tumor versus normal tissue (log2 FC = +1.445, t-test p < 0.001).
This table shows molecular features associated with SCML1 in patient tissues and cancer cell lines. In patient samples, SCML1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, SCML1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
