RAS like proto-oncogene AGenealiases: HINCONS · RAL
Q-omics provides the consensus-scored RALA profile across patient tissues and cancer cell-line models. RALA expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, RALA is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, RALA protein abundance shows 22,518 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, KIRC, and GBM as cancer lineages where RALA 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 RALA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RALA survival associations across molecular data types. RALA RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RALA RNA expression–survival associations across cancer types. High RALA expression shows unfavorable associations in MESO, LIHC, PAAD, KICH and KIRP, but favorable associations in KIRC. The MESO 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 MESO as the clearest survival context for RALA RNA expression.
This table summarizes RALA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for RALA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RALA shows higher tumor expression in KIRC, LIHC, KIRP, BLCA, KICH and HNSC. The KIRC box plot shows higher RALA RNA expression in tumor versus normal tissue (log2 FC = +0.539, t-test p < 0.001).
This table shows molecular features associated with RALA in patient tissues and cancer cell lines. In patient samples, RALA 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, RALA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Lymphoma.
