Provided are a method for generating a data set for integrated proteomics analysis, whereby expression level variations of both of proteins and genes can be integrally united together and, moreover, highly accurate and appropriate analysis results can be obtained compared with the existing cases where the expression variation amount of proteins or genes is singly analyzed, an integrated proteomics analysis method, a method for identifying a protein causative of a disease or the like using these methods, and a method of using the same???. The aforesaid method for generating a data set for integrated proteomics analyses comprises: a protein identity number-assigning step for assigning common identity numbers to the expression variation amount data of individual proteins; a gene identity number-assigning step for assigning common identity numbers to the expression variation amount data of individual genes; a data-binding step for binding together the set of the expression variation amount data; a data-rejecting step for rejecting, among the individual expression variation amount data constituting the thus bound data set, data showing a p-value equal to or greater than a specific level; and a data-selecting step for selecting one data, from a set of data having the same common identity number assigned thereto, on the basis of a definite requirement to thereby generate the data set to be subjected to integrated proteomics analyses. Further, the data set for integrated proteomics analyses thus generated is subjected to GO analysis and network analysis to thereby identify a protein causative of a disease, a pathological condition or the like. Furthermore, the causative protein thus identified is usable, for example, a tumor marker or a clinical target.