high mobility group box 3 pseudogene 1Genealiases: HMG4L · HMGB3L1 · dJ18C9.3
Q-omics provides the consensus-scored HMGB3P1 profile across patient tissues and cancer cell-line models. HMGB3P1 expression is associated with patient survival in 16 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, HMGB3P1 is differentially expressed in 4, with the highest sampling consensus in THCA. Additionally, HMGB3P1 RNA expression shows 6,564 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight KICH, THCA, and STAD as cancer lineages where HMGB3P1 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 HMGB3P1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HMGB3P1 survival associations across molecular data types. HMGB3P1 RNA expression shows survival associations in the most cancer types (16), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HMGB3P1 RNA expression–survival associations across cancer types. High HMGB3P1 expression shows unfavorable associations in KICH, ACC, UCEC, SKCM, COAD and ESCA. 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 HMGB3P1 RNA expression.
This table summarizes HMGB3P1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 4. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for HMGB3P1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HMGB3P1 shows lower tumor expression in THCA and higher tumor expression in ESCA, LUAD and LUSC. The THCA box plot shows higher HMGB3P1 RNA expression in normal versus tumor tissue (log2 FC = −0.077, t-test p = .004).
This table shows molecular features associated with HMGB3P1 in patient tissues and cancer cell lines. In patient samples, HMGB3P1 shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, HMGB3P1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and NCI60_ALL.
