Q-omics provides the consensus-scored AXL profile across patient tissues and cancer cell-line models. AXL expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, AXL is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, AXL RNA expression shows 21,566 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, KIRC, and LSCC as cancer lineages where AXL 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 AXL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AXL survival associations across molecular data types. AXL RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AXL RNA expression–survival associations across cancer types. High AXL expression shows unfavorable associations in MESO, STAD, LGG, BLCA and KIRC, but favorable associations in SKCM. 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 AXL RNA expression.
This table summarizes AXL 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 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for AXL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AXL shows lower tumor expression in COAD, BLCA, LUSC and UCEC and higher tumor expression in KIRC and HNSC. The KIRC box plot shows higher AXL RNA expression in tumor versus normal tissue (log2 FC = +1.830, t-test p < 0.001).
This table shows molecular features associated with AXL in patient tissues and cancer cell lines. In patient samples, AXL 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, AXL 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 OVARY and BONE.
