Motivation: Protein abundance profiles often show similar alterations across different cardiac diseases, limiting the ability to discriminate pathologies based on individual protein changes. Patients with the same clinical diagnosis frequently display heterogeneous proteomic signatures, the biological basis of which remains unclear. Objective: This study aimed to identify molecular subprofiles defined by coordinated alterations in specific groups of proteins, with the goal of uncovering patient level patterns that may explain differential manifestations of the same cardiac disease. Methods: A total of 150 human cardiac tissue samples (controls, arrhythmogenic, hypertrophic and dilated cardiomyopathy) were analyzed using TMTpro based quantitative proteomics. The dataset comprised ~2900 proteins. Those with >30% missing values were removed and remaining missing values were imputed with a low abundance value. Z score-normalized abundances were subjected to hierarchical clustering to identify groups of proteins with similar expression patterns. Functional annotation was performed using the UniProt Knowledgebase (UniProtKB), the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to determine subcellular localization, biological processes, molecular functions and pathway involvement. Functional enrichment analysis was conducted with gseapy Python package. External validation of disease associations was obtained through the Open Targets platform. Results: Hierarchical clustering of biological processes identified three protein clusters. Cluster 2 (33 proteins), mainly associated with cardiac muscle contraction and sarcomere organization, showed the strongest functional enrichment (p-values 7.3e-27 to 9.8e-08), followed by cluster 1, associated with respiration and metabolic processes (55 proteins, 4.7e-11 to 2.4e-04), and cluster 3, associated with muscle contraction, cell differentiation and cardiac development (70 proteins, 1.1e-5 to 3.7e-3). Clustering proteins by disease did not produce distinct or well defined groups, nor did clustering by protein subcellular localization. Conclusions: Coordinated protein patterns, rather than individual protein abundances, can provide an informative framework for characterizing molecular variability across cardiac diseases.