COPG1 Is a Selectively Essential Regulator of Cancer Progression and Chemoresistance: A Comprehensive Pan-Cancer Study

The coatomer complex, a key regulator of intracellular vesicular transport, has recently been implicated in cancer progression, yet a comprehensive pan-cancer analysis of its subunits has been lacking. Here, we systematically profiled genetic alterations, expression patterns, prognostic relevance, and functional dependencies of all COPI and COPII coatomer subunits across 33 cancer types using more than 10,000 tumor samples from The Cancer Genome Atlas (TCGA), complemented by functional perturbation data from CRISPR (n = 1,178) and RNAi (n = 707) screens in DepMap. Gene amplification—most notably of COPB2—was the most frequent alteration and was associated with poor survival in bladder and esophageal cancers. Mutations in COPA and SEC31A also demonstrated prognostic significance in endometrial carcinoma. Expression analyses revealed broad upregulation of coatomer genes across cancer types, with COPG1 and COPB1 emerging as strong risk-associated genes (HR > 2). Integrative functional dependency analyses further identified COPG1 and COPE as selectively essential in multiple cancer contexts, with COPG1 loss notably linked to increased drug sensitivity. Functional validation in hepatocellular carcinoma (HCC) showed that COPG1 knockdown impaired malignant phenotypes and reduced tumorigenicity in vivo. Mechanistically, COPG1 depletion induced Golgi disruption and ER stress, increased ROS production, and suppressed PI3K–AKT signaling. Moreover, COPG1 knockdown sensitized HCC cells to sorafenib and doxorubicin, whereas COPG1 overexpression conferred resistance. Collectively, our pan-cancer analysis reveals context-dependent roles of coatomer subunits and identifies COPG1 as a novel oncogenic driver and potential therapeutic target in HCC, mediating chemoresistance through redox modulation and PI3K–AKT pathway inhibition. Overall design: Total RNA was extracted using the RNeasy Mini Kit (Qiagen). RNA quality was assessed by running 1 µl of RNA on a Bioanalyzer system (Agilent, CA, USA) to ensure the RIN and rRNA ratio. We used 100 ng of total RNA to prepare the sequencing libraries using the MGIEasy RNA Directional Library prep kit (MGI). They were quantified using the Agilent 2100 BioAnalyzer (Agilent) according to the manufacturer's library quantification protocol. Following the cluster amplification of the denatured templates, sequencing was performed as paired-end (2 × 150 bp) using the MGI DNBSEQ-T7 platform. Data processing was performed as previously reported(15). Briefly, raw sequencing reads were processed by FastQC (RRID:SCR_014583) and aligned by STAR (RRID:SCR_004463) to the human reference genome hg38 with Ensembl annotation. Transcript abundance was quantified using RSEM5 in the units of TPM. Differential gene expression analysis was performed using DEseq2 (RRID:SCR_015687).