Q-omics provides the consensus-scored H2BC1 profile across patient tissues and cancer cell-line models. H2BC1 expression is associated with patient survival in 14 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, H2BC1 is differentially expressed in 2, with the highest sampling consensus in LIHC. Additionally, H2BC1 RNA expression shows 9,634 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KICH, LIHC, and TGCT as cancer lineages where H2BC1 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 H2BC1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes H2BC1 survival associations across molecular data types. H2BC1 RNA expression shows survival associations in the most cancer types (14), followed by mutation status (1) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible H2BC1 RNA expression–survival associations across cancer types. High H2BC1 expression shows unfavorable associations in KICH, READ, THCA, COAD and BLCA, but favorable associations in KIRP. The KICH 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 KICH as the clearest survival context for H2BC1 RNA expression.
This table summarizes H2BC1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 2, while mass-spec protein shows differences in 2. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for H2BC1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. H2BC1 shows higher tumor expression in LIHC and LUAD. The LIHC box plot shows higher H2BC1 RNA expression in tumor versus normal tissue (log2 FC = +0.090, t-test p = .006).
This table shows molecular features associated with H2BC1 in patient tissues and cancer cell lines. In patient samples, H2BC1 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, H2BC1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and SKIN.
