Negative regulation of peptidyl-cysteine S-nitrosylation

associated omics data
GO:1902083Ontology (GO BP)GO biological process · ~8 member genes

Q-omics provides the Negative regulation of peptidyl-cysteine S-nitrosylation (GO:1902083) pathway profile, scoring each patient from the combined activity of its roughly 8 member genes. Pathway activity is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 14, with the highest sampling consensus in KIRC. Additionally, pathway RNA activity shows 35,535 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight LUAD, KIRC, and STAD as cancer lineages where the pathway shows reproducible signals across outcome, tissue activity, and molecular association 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. Pathway-against-pathway and pathway-against-mutation comparisons are not available for ontology entities.

Survival associations

This table summarizes Negative regulation of peptidyl-cysteine S-nitrosylation survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (22). The rightmost column indicates the cancer type with the highest sampling consensus for each layer.
Data typeSurvival analysisLineage consensusLineage of highest sampling consensus
GO function (RNA)Kaplan–Meier22LUAD (86)view →
GO function (Protein (mass-spec))Kaplan–Meier4LUAD (14)view →
This table ranks reproducible pathway activity–survival associations across cancer types. High Negative regulation of peptidyl-cysteine S-nitrosylation activity shows favorable associations in LUAD, ACC and KICH, but unfavorable associations in BRCA, BLCA and DLBC. In the LUAD Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p = .001). LUAD ranks highest by sampling consensus for Negative regulation of peptidyl-cysteine S-nitrosylation.
LineageMeasureSplitStageAUC1
high
AUC2
low
pSampling consensus
LUADDFSMedianAll0.8420.694.00186view →
ACCOSQuartileII,III,IV0.8800.338.00351view →
BRCAOSTertileIII,IV0.3160.735.00248view →
KICHOSTertileIII,IV1.0000.739.00739view →
BLCAOSTertileII,III,IV0.2970.508.01038view →
DLBCOSMedianAll0.7361.000.00433view →
Pink = unfavorable, green = favorable. all 22 lineages →

Tumor vs Normal activity

This table summarizes Negative regulation of peptidyl-cysteine S-nitrosylation tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 14 cancer types, while mass-spec protein activity shows differences in 5. The strongest signals are in KIRC for RNA and COAD for protein.
Data typeActivity analysisLineage consensusLineage of highest sampling consensus
GO function (RNA)Box plot14KIRC (12)view →
GO function (Protein (mass-spec))Box plot5COAD (10)view →
This table ranks reproducible tumor–normal activity differences for the pathway. A positive fold-change indicates higher activity in tumor tissue. The pathway shows consistently lower tumor activity across KIRC, HNSC, BLCA, KIRP, BRCA and UCEC. In the KIRC box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.101, t-test p < 0.001).
LineageGenderStageFold-changepSampling consensus
KIRCFemaleIV−0.101<.00112view →
HNSCMaleAll−0.070<.00111view →
BLCAFemaleIV−0.166<.0018view →
KIRPFemaleAll−0.084<.0018view →
BRCAAllIII,IV−0.109<.0016view →
UCECAllAll−0.068<.0016view →
Pink = higher activity in tumor. all 14 lineages →

Cross-omics associations

This table shows molecular features associated with Negative regulation of peptidyl-cysteine S-nitrosylation pathway activity in patient tissues and cancer cell lines. In patient samples, pathway activity is most strongly linked to RNA and protein features, with the largest associated set in STAD. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in SKIN.
Associated data typeStrength (# associated data)Lineage of highest associated data
RNA
RNA35,535STAD (17808)view →
Protein (mass-spec)11,341GBM (3551)view →
Protein (mass-spec)
Protein (mass-spec)21,827LSCC (7614)view →
RNA7,515LSCC (5404)view →
Associated data typeStrength (# associated data)Lineage of highest associated data
shRNA
shRNA1,891SKIN (190)view →
RNA1,639LUNG_NSCLC_LUSC (274)view →
RNA
Inducing drug2NCI60_ALL (2)view →