For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts. By relating the modules to the donor's traits, we uncovered the natural aging- and premature aging disease-associated modules. The STRING functional association networks built with the core module memberships provided a systematic overview of genome-wide transcriptional changes upon aging. We validated the selected candidates via quantitative reverse transcription PCR (RT-qPCR) assay with young and aged human fibroblasts, and uncovered several genes involved in ECM, cell, and nuclear mechanics as a potential aging biomarker. Collectively, our study not only provides a snapshot of functional changes during human fibroblast aging but also presents potential aging markers that are relevant to cell mechanics.