Q-omics provides the consensus-scored RGMA profile across patient tissues and cancer cell-line models. RGMA expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, RGMA is differentially expressed in 15, with the highest sampling consensus in THCA. Additionally, RGMA RNA expression shows 18,788 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, THCA, and LSCC as cancer lineages where RGMA 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 RGMA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RGMA survival associations across molecular data types. RGMA RNA expression shows survival associations in the most cancer types (22), followed by mutation status (10) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RGMA RNA expression–survival associations across cancer types. High RGMA expression shows unfavorable associations in BLCA and KIRP, but favorable associations in HNSC, LUSC, UCS and UCEC. 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 RGMA RNA expression.
This table summarizes RGMA 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 1. The strongest signals are observed in THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for RGMA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RGMA shows lower tumor expression in THCA, KICH, COAD, HNSC and KIRP and higher tumor expression in LIHC. The THCA box plot shows higher RGMA RNA expression in normal versus tumor tissue (log2 FC = −0.789, t-test p < 0.001).
This table shows molecular features associated with RGMA in patient tissues and cancer cell lines. In patient samples, RGMA 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, RGMA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
