Q-omics provides the consensus-scored MICALL2 profile across patient tissues and cancer cell-line models. MICALL2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, MICALL2 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, MICALL2 protein abundance shows 20,696 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, and LSCC as cancer lineages where MICALL2 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 MICALL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MICALL2 survival associations across molecular data types. MICALL2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) 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 MICALL2 RNA expression–survival associations across cancer types. High MICALL2 expression shows unfavorable associations in KIRC, MESO, UVM, ACC, LGG and LIHC. The KIRC 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 KIRC as the clearest survival context for MICALL2 RNA expression.
This table summarizes MICALL2 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 PDAC for protein.
This table ranks reproducible tumor–normal expression differences for MICALL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MICALL2 shows higher tumor expression in KIRC, HNSC, THCA, LUAD, COAD and STAD. The KIRC box plot shows higher MICALL2 RNA expression in tumor versus normal tissue (log2 FC = +1.192, t-test p < 0.001).
This table shows molecular features associated with MICALL2 in patient tissues and cancer cell lines. In patient samples, MICALL2 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, MICALL2 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 URINARY_TRACT and CNS.
