Q-omics provides the consensus-scored HSPA1A profile across patient tissues and cancer cell-line models. HSPA1A expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, HSPA1A is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, HSPA1A RNA expression shows 13,199 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight COAD, KICH, and ACC as cancer lineages where HSPA1A 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 HSPA1A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HSPA1A survival associations across molecular data types. HSPA1A RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HSPA1A RNA expression–survival associations across cancer types. High HSPA1A expression shows unfavorable associations in COAD, MESO, ACC, READ, LUAD and LIHC. The COAD 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 COAD as the clearest survival context for HSPA1A RNA expression.
This table summarizes HSPA1A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for HSPA1A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HSPA1A shows lower tumor expression in KICH, COAD, UCEC, KIRC and THCA and higher tumor expression in LIHC. The KICH box plot shows higher HSPA1A RNA expression in normal versus tumor tissue (log2 FC = −3.613, t-test p < 0.001).
This table shows molecular features associated with HSPA1A in patient tissues and cancer cell lines. In patient samples, HSPA1A shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, HSPA1A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
