Q-omics provides the consensus-scored HABP2 profile across patient tissues and cancer cell-line models. HABP2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, HABP2 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, HABP2 protein abundance shows 18,265 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, KICH, and GBM as cancer lineages where HABP2 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 HABP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HABP2 survival associations across molecular data types. HABP2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (2) 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 HABP2 RNA expression–survival associations across cancer types. High HABP2 expression shows unfavorable associations in STAD and LGG, but favorable associations in KIRC, UVM, BLCA and LUAD. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for HABP2 RNA expression.
This table summarizes HABP2 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 5. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for HABP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HABP2 shows lower tumor expression in KICH, LIHC and LUSC and higher tumor expression in COAD, LUAD and READ. The KICH box plot shows higher HABP2 RNA expression in normal versus tumor tissue (log2 FC = −3.252, t-test p < 0.001).
This table shows molecular features associated with HABP2 in patient tissues and cancer cell lines. In patient samples, HABP2 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, HABP2 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 BLOOD_Leukemia and LARGE_INTESTINE.
