Q-omics provides the consensus-scored SMARCB1 profile across patient tissues and cancer cell-line models. SMARCB1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, SMARCB1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, SMARCB1 protein abundance shows 26,383 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, HNSC, and GBM as cancer lineages where SMARCB1 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 SMARCB1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SMARCB1 survival associations across molecular data types. SMARCB1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 SMARCB1 RNA expression–survival associations across cancer types. High SMARCB1 expression shows unfavorable associations in LIHC, ACC, KICH and MESO, but favorable associations in SCLC and KIRC. The LIHC 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 LIHC as the clearest survival context for SMARCB1 RNA expression.
This table summarizes SMARCB1 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 4. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SMARCB1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SMARCB1 shows higher tumor expression in HNSC, BLCA, COAD, LIHC, LUSC and LUAD. The HNSC box plot shows higher SMARCB1 RNA expression in tumor versus normal tissue (log2 FC = +1.014, t-test p < 0.001).
This table shows molecular features associated with SMARCB1 in patient tissues and cancer cell lines. In patient samples, SMARCB1 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, SMARCB1 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 BLOOD_Leukemia and SOFT_TISSUE.
