Q-omics provides the consensus-scored PKIG profile across patient tissues and cancer cell-line models. PKIG expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, PKIG is differentially expressed in 16, with the highest sampling consensus in BLCA. Additionally, PKIG protein abundance shows 23,993 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LUSC, BLCA, and LSCC as cancer lineages where PKIG 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 PKIG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PKIG survival associations across molecular data types. PKIG RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) 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 PKIG RNA expression–survival associations across cancer types. High PKIG expression shows unfavorable associations in LUSC and SCLC, but favorable associations in KIRC, SKCM, MESO and PAAD. The LUSC 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 LUSC as the clearest survival context for PKIG RNA expression.
This table summarizes PKIG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PKIG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PKIG shows lower tumor expression in BLCA, KICH, LUSC, LUAD, COAD and UCEC. The BLCA box plot shows higher PKIG RNA expression in normal versus tumor tissue (log2 FC = −2.941, t-test p < 0.001).
This table shows molecular features associated with PKIG in patient tissues and cancer cell lines. In patient samples, PKIG 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, PKIG 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 BLOOD_Leukemia and LUNG_NSCLC_LUAD.
