Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine, because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment approaches. One of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic development. Systematic accumulation of various types of biological data, including interactions between proteins, regulation of genes transcription, proteomics, and metabolomics experiments results, allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic targets. The aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components, as well as for identifying their roles in the development of various types of tumors. We automatically collect sets of abstract texts from the NCBI PubMed bibliographic database. For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach, while for all other proteins and genes machine learning method is used. For association extraction, we develop a set of semantic rules. We complete the results of the text analysis with the gene set enrichment analysis. The identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.