Q-omics provides the consensus-scored RGS2 profile across patient tissues and cancer cell-line models. RGS2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, RGS2 is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, RGS2 RNA expression shows 19,891 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight STAD, KICH, and GBM as cancer lineages where RGS2 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 RGS2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RGS2 survival associations across molecular data types. RGS2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RGS2 RNA expression–survival associations across cancer types. High RGS2 expression shows unfavorable associations in STAD, KIRP, BLCA and COAD, but favorable associations in UCS and SKCM. The STAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify STAD as the clearest survival context for RGS2 RNA expression.
This table summarizes RGS2 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 2. The strongest signals are observed in BLCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for RGS2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RGS2 shows lower tumor expression in KICH, BLCA, UCEC, BRCA, COAD and STAD. The KICH box plot shows higher RGS2 RNA expression in normal versus tumor tissue (log2 FC = −3.867, t-test p < 0.001).
This table shows molecular features associated with RGS2 in patient tissues and cancer cell lines. In patient samples, RGS2 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, RGS2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and BREAST.
