Q-omics provides the consensus-scored AIG1 profile across patient tissues and cancer cell-line models. AIG1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, AIG1 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, AIG1 RNA expression shows 18,833 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight HNSC, KICH, and THYM as cancer lineages where AIG1 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 AIG1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AIG1 survival associations across molecular data types. AIG1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (1) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AIG1 RNA expression–survival associations across cancer types. High AIG1 expression shows unfavorable associations in HNSC, KICH, OV, CESC and MESO, but favorable associations in KIRC. The HNSC 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 HNSC as the clearest survival context for AIG1 RNA expression.
This table summarizes AIG1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 3. The strongest signals are observed in KICH for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for AIG1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AIG1 shows lower tumor expression in KICH, LIHC and THCA and higher tumor expression in KIRC, HNSC and COAD. The KICH box plot shows higher AIG1 RNA expression in normal versus tumor tissue (log2 FC = −2.117, t-test p < 0.001).
This table shows molecular features associated with AIG1 in patient tissues and cancer cell lines. In patient samples, AIG1 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, AIG1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
