Q-omics provides the consensus-scored ASL profile across patient tissues and cancer cell-line models. ASL expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, ASL is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, ASL protein abundance shows 22,665 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight CESC, KICH, and LSCC as cancer lineages where ASL 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 ASL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASL survival associations across molecular data types. ASL RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASL RNA expression–survival associations across cancer types. High ASL expression shows unfavorable associations in CESC, UVM, MESO, ACC, LGG and LUSC. The CESC 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 CESC as the clearest survival context for ASL RNA expression.
This table summarizes ASL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 8. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ASL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASL shows lower tumor expression in KICH, LUSC and KIRP and higher tumor expression in UCEC, BLCA and ESCA. The KICH box plot shows higher ASL RNA expression in normal versus tumor tissue (log2 FC = −1.442, t-test p < 0.001).
This table shows molecular features associated with ASL in patient tissues and cancer cell lines. In patient samples, ASL 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, ASL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
