Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer.

Objective: The objective of this study is to detect functionally significant modules from gene coexpression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer.

Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer.

Results and Discussion: Predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer.

Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage- III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III, respectively.

背景：乳腺癌是全球女性最常见的癌症，发病率和死亡率都很高。作为一种异质性疾病，基于基因表达谱的分析在理解乳腺癌方面发挥着重要作用。由于属于同一阶段乳腺癌患者的表达模式差异很大，因此涉及多个样本的综合分期分析有望提供更全面的结果和对乳腺癌的了解。

目的：本研究的目的是从癌组织的基因共表达网络中检测功能显着的模块，并提取与乳腺癌多个阶段相关的预后基因。

方法：为了实现这一点，我们对多重框架进行建模以绘制乳腺癌的多个阶段，然后采用模块化优化方法从中识别功能模块。发现这些功能模块显着丰富了许多与癌症相关的基因本体论术语。

结果和讨论：基于乳腺癌多个阶段的差异表达分析来鉴定预测性生物标志物。

结论：我们的分析确定了 13 个 I 期特异性基因、12 个 II 期特异性基因和 42 个 III 期特异性基因，这些基因受到显着调控，可能是乳腺癌治疗的有希望的靶点。除此之外，我们可以分别识别出 29、18 和 26 个特定于阶段 I、阶段 II 和阶段 III 的 lncRNA。