RNA-seq of gastrocnemius muscle of orthotopic lung cancer mice with cachexia and sham control.

Cancer cachexia, highly prevalent in lung cancer, is a debilitating syndrome characterized by involuntary loss of skeletal muscle mass, and is associated with poor clinical outcome, decreased survival and negative impact on tumor therapy. Various lung tumor-bearing animal models have been used to explore underlying mechanisms of cancer cachexia. However, these models do not simulate anatomical and immunological features key to lung cancer and associated muscle wasting. Overcoming these shortcomings is essential to translate experimental findings into the clinic. We therefore evaluated whether a syngeneic, orthotopic lung cancer cachexia (OLCC) mouse model replicates systemic and muscle-specific alterations associated with human lung cancer cachexia. Immune competent, 11 weeks old male 129S2/Sv mice, were randomly allocated to either (1) sham control group or (2) tumor-bearing (OLCC) group. Syngeneic lung epithelium-derived adenocarcinoma cells (K-rasG12D; p53R172H?G) were inoculated intrapulmonary into the left lung lobe of the mice. Body weight and food intake were measured daily. At baseline and weekly after surgery, grip strength was measured and tumor growth and muscle volume were assessed using micro cone beam CT imaging. After reaching predefined surrogate survival endpoint, animals were euthanized and skeletal muscles of the lower hind limbs were collected forRNA sequencing. RNA sequencing was performed on the Illumina NovasSeq 6000.

肺癌患者中高发的癌症恶病质（Cancer cachexia）是一类以骨骼肌质量非自主性丢失为核心特征的衰弱综合征，与不良临床结局、生存期缩短及肿瘤治疗受负面影响密切相关。既往已有多种肺癌荷瘤动物模型被用于探究癌症恶病质的潜在发病机制，但此类模型均无法模拟肺癌及其相关肌肉消耗的关键解剖学与免疫学特征。克服上述局限对于将实验发现转化至临床应用至关重要，为此本研究评估了同源原位肺癌恶病质（OLCC）小鼠模型能否复现人类肺癌恶病质相关的系统性及骨骼肌特异性改变。
选取免疫健全的11周龄雄性129S2/Sv小鼠，随机分为两组：(1) 假手术对照组，(2) 荷瘤（OLCC）组。将同源肺上皮源性腺癌细胞（K-rasG12D；p53R172H?G）经肺内接种至小鼠左肺叶。每日记录小鼠体重与进食量；于术前基线及术后每周检测握力，并采用微型锥形束CT（micro cone beam CT）评估肿瘤生长情况与肌肉体积。待小鼠达到预设替代生存终点后，实施安乐死并采集其后肢骨骼肌组织用于RNA测序（RNA sequencing），后续RNA测序实验在Illumina NovasSeq 6000平台上完成。