HNRNPA1P48

associated omics data
Gene

Q-omics provides the consensus-scored HNRNPA1P48 profile across patient tissues and cancer cell-line models. HNRNPA1P48 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, HNRNPA1P48 is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, HNRNPA1P48 RNA expression shows 18,287 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and COAD as cancer lineages where HNRNPA1P48 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.

Survival associations

This table summarizes HNRNPA1P48 survival associations across molecular data types. HNRNPA1P48 RNA expression shows survival associations in the most cancer types (24). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
HNRNPA1P48 data typeSurvival analysisLineage consensusLineage of highest sampling consensus
RNAKaplan–Meier24ACC (126)view →
This table ranks reproducible HNRNPA1P48 RNA expression–survival associations across cancer types. High HNRNPA1P48 expression shows unfavorable associations in ACC, LIHC, KIRP and LUAD, but favorable associations in LUSC and UCEC. The ACC 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 ACC as the clearest survival context for HNRNPA1P48 RNA expression.
LineageMeasureSplitStageAUC1
high
AUC2
low
pSampling consensus
ACCDFSMedianAll0.2180.672<.001126view →
LIHCDFSTertileAll0.3290.563<.00179view →
KIRPOSTertileAll0.8760.992<.00165view →
LUADOSQuartileAll0.7360.868.00255view →
LUSCOSTertileAll0.7460.589<.00151view →
UCECDFSTertileAll0.8830.768.00148view →
Pink = unfavorable, green = favorable. all 24 lineages →

HNRNPA1P48-ACC (DFS)

Kaplan–Meier survival curve for HNRNPA1P48 RNA expression in ACC: high vs low expression groups.

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Tumor vs Normal expression

This table summarizes HNRNPA1P48 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in COAD for RNA.
HNRNPA1P48 data typeExpression analysisLineage consensusLineage of highest sampling consensus
RNABox plot13COAD (11)view →
This table ranks reproducible tumor–normal expression differences for HNRNPA1P48. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HNRNPA1P48 shows higher tumor expression in COAD, KIRC, LIHC, STAD, LUSC and HNSC. The COAD box plot shows higher HNRNPA1P48 RNA expression in tumor versus normal tissue (log2 FC = +1.318, t-test p < 0.001).
LineageGenderStageFold-changepSampling consensus
COADMaleIII,IV+1.318<.00111view →
KIRCMaleAll+0.540<.00110view →
LIHCFemaleII,III,IV+0.978<.0019view →
STADFemaleAll+1.108<.0018view →
LUSCAllII,III,IV+0.481<.0017view →
HNSCMaleAll+0.459<.0016view →
Green = repressed in tumor. all 13 lineages →

HNRNPA1P48-COAD

Tumor-vs-normal expression box plot for HNRNPA1P48 in COAD.

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Cross-omics associations

This table shows molecular features associated with HNRNPA1P48 in patient tissues and cancer cell lines. In patient samples, HNRNPA1P48 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set.
Associated data typeStrength (# associated data)Lineage of highest associated data
RNA
RNA18,287ACC (7535)view →
Protein (mass-spec)8,965LSCC (1967)view →