Q-omics provides the consensus-scored GSE1 profile across patient tissues and cancer cell-line models. GSE1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, GSE1 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, GSE1 protein abundance shows 28,848 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UCS, COAD, and LSCC as cancer lineages where GSE1 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 GSE1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GSE1 survival associations across molecular data types. GSE1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GSE1 RNA expression–survival associations across cancer types. High GSE1 expression shows unfavorable associations in MESO and STAD, but favorable associations in UCS, LGG, SCLC and PAAD. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .018). Together, the overview and detailed table identify UCS as the clearest survival context for GSE1 RNA expression.
This table summarizes GSE1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 10. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GSE1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GSE1 shows lower tumor expression in LUSC and higher tumor expression in COAD, LIHC, KIRP, STAD and HNSC. The COAD box plot shows higher GSE1 RNA expression in tumor versus normal tissue (log2 FC = +0.580, t-test p < 0.001).
This table shows molecular features associated with GSE1 in patient tissues and cancer cell lines. In patient samples, GSE1 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, GSE1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
