regenerating family member 3 gammaGenealiases: LPPM429 · PAP IB · PAP-1B · PAP1B · PAPIB · REG III
Q-omics provides the consensus-scored REG3G profile across patient tissues and cancer cell-line models. REG3G expression is associated with patient survival in 17 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, REG3G is differentially expressed in 8, with the highest sampling consensus in KIRP. Additionally, REG3G RNA expression shows 9,205 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight CESC, KIRP, and ESCA as cancer lineages where REG3G 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 REG3G — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes REG3G survival associations across molecular data types. REG3G RNA expression shows survival associations in the most cancer types (17), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible REG3G RNA expression–survival associations across cancer types. High REG3G expression shows unfavorable associations in CESC, LUAD, UVM, UCEC and DLBC, but favorable associations in CHOL. The CESC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify CESC as the clearest survival context for REG3G RNA expression.
This table summarizes REG3G tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRP for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for REG3G. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. REG3G shows lower tumor expression in KICH, BRCA and STAD and higher tumor expression in KIRP, LIHC and COAD. The KIRP box plot shows higher REG3G RNA expression in tumor versus normal tissue (log2 FC = +2.194, t-test p = .001).
This table shows molecular features associated with REG3G in patient tissues and cancer cell lines. In patient samples, REG3G shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, REG3G RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in CNS and STOMACH.
