The coatomer complex has been implicated in cancer progression; however, a comprehensive pan-cancer analysis is lacking. Therefore, it is essential to identify the critical roles and essentiality of coatomer genes across pan-cancer. We systematically profiled the genetic alterations, expression patterns, prognostic relevance, and functional dependencies of all coatomer subunits across multiple cancers using more than 10,000 tumor samples from The Cancer Genome Atlas, complemented by functional perturbation data from CRISPR (n = 1178) and RNAi (n = 707) screens in DepMap. Functional validation was also performed to identify the essentiality of selectively essential coatomer genes in hepatocellular carcinoma (HCC). 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 identified COPG1 as selectively essential in multiple cancers, and its loss was associated with increased drug sensitivity. Functional validation in hepatocellular carcinoma revealed 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, thereby sensitizing cells to sorafenib and doxorubicin. Collectively, this pan-cancer analysis reveals the 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.

Background and Objectives: Frailty is a multidimensional vulnerability in older adults; the Fried phenotype and Frailty Index are clinically informative but labor-intensive, limiting scalability for community screening. Machine learning (ML) can model heterogeneous, high-dimensional data, but real-world adoption is constrained by heterogeneity in definitions, predictors, validation strategies, and explainability. We systematically synthesized ML-based studies of frailty prediction and classification in community-dwelling older adults, examining validation rigor, explainability, and implementation readiness. Methods: This systematic review followed PRISMA 2020 and was registered in PROSPERO (CRD420251081555). PubMed, Embase, Web of Science, and Scopus were searched on 4 July 2025, with a supplementary IEEE Xplore and ACM Digital Library search conducted on 12 May 2026. Eligible studies included community-dwelling adults aged ≥60 years, ML-based frailty prediction or classification, sample ≥ 1000, and publication in a peer-reviewed journal indexed in the Web of Science Core Collection; hospital-based studies were excluded. Risk of bias and reporting quality were assessed with PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis); implementation readiness was assessed with the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework and a Technology Readiness Level (TRL)-style rubric. Findings were synthesized narratively. Results: Fourteen studies (development cohorts 1230–86,133 participants) were included; the supplementary IEEE/ACM search identified 42 records but yielded no additional eligible studies. Classification of current frailty status (n = 7) yielded AUROCs (area under the receiver operating characteristic curve) of 0.70–0.98, with the highest values likely reflecting partial label overlap with frailty components; incident prediction (n = 6) yielded internal AUROCs of 0.70–0.81 and same-cohort temporal AUROCs of 0.58–0.85; independent external validation was uncommon. Only 2 of 14 studies had both low overall risk of bias and low applicability concern (PROBAST); the field is concentrated at TRL 4–6, with no study at TRL 7 or higher and none documenting Implementation or Maintenance domains of RE-AIM. Conclusions: ML-based frailty models show heterogeneous discrimination and limited readiness for routine community use. Priorities include standardized task-type-specific definitions, independent external validation, calibration and decision-curve reporting, transparent predictor disclosure, and prospective implementation evaluation.

Intramedullary cervical spinal cord abscess mimicking spinal cord hematoma: a case report