Q-omics provides the consensus-scored ICA1L profile across patient tissues and cancer cell-line models. ICA1L expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, ICA1L is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, ICA1L RNA expression shows 21,542 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight BLCA, KICH, and GBM as cancer lineages where ICA1L 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 ICA1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ICA1L survival associations across molecular data types. ICA1L RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ICA1L RNA expression–survival associations across cancer types. High ICA1L expression shows unfavorable associations in UVM and KICH, but favorable associations in BLCA, BRCA, PAAD and LUAD. The BLCA Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .004). Together, the overview and detailed table identify BLCA as the clearest survival context for ICA1L RNA expression.
This table summarizes ICA1L 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 2. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ICA1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ICA1L shows lower tumor expression in KICH, BLCA, UCEC, LUAD and BRCA and higher tumor expression in LIHC. The KICH box plot shows higher ICA1L RNA expression in normal versus tumor tissue (log2 FC = −1.271, t-test p < 0.001).
This table shows molecular features associated with ICA1L in patient tissues and cancer cell lines. In patient samples, ICA1L 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, ICA1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
