INTRODUCTION
The landscape of oncology has undergone a profound transformation with the advent of molecular approaches that identify key genetic markers and assist imaging techniques for diagnosis. Pancreatic cancer and esophageal squamous cell carcinoma (ESCC) collectively claim over 1.1 million lives annually worldwide, with pancreatic malignancies responsible for > 500000 deaths and ESCC for > 600000. Both exhibit exceptionally poor prognoses (5-year survival < 20%), driven by distinct pathobiological challenges: Pancreatic cancer’s insidious onset and early metastatic propensity contrast with ESCC’s strong geographic clustering (Asian predominance) and modifiable risk factors (tobacco/alcohol use). Their shared clinical hurdle - late-stage diagnosis due to nonspecific symptoms - underscores the urgent need for enhanced early detection strategies and molecularly targeted interventions[1]. These innovations have fundamentally shifted the paradigm of cancer care, enabling earlier detection and leading to more personalized treatment protocols[2]. Precision oncology, which integrates genetic and molecular profiling, is now central to diagnosing and treating cancers, especially those with complex genetic backgrounds such as ESCC and pancreatic cancer. Advances in next-generation sequencing have not only accelerated mutation detection but also deepened our understanding of tumor microenvironments. For instance, recent studies in breast cancer highlight how next-generation sequencing reveals exhausted T-cell markers linked to immunotherapy resistance[3]. Similarly, breakthroughs in imaging technology - such as high-resolution computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS) - allow for more accurate visualization of tumor characteristics, improving diagnosis and treatment planning[4,5].
Two recent studies published in the World Journal of Gastrointestinal Oncology underscore the critical importance of these innovations. Lin et al[6] focus on genetic markers in ESCC, investigating the role of single nucleotide polymorphisms (SNPs) in the activin A receptor type 1C (ACVR1C) gene, which may predispose individuals to cancer through its involvement in the transforming growth factor-beta (TGF-β) signaling pathway. Their findings could lead to novel genetic screening tools and personalized prevention strategies for high-risk populations. Meanwhile, Luo et al[7] explore imaging markers that differentiate pathological types of pancreatic cancer, highlighting the distinct imaging features of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine tumors. This study emphasizes the utility of integrating multiple imaging modalities to refine diagnoses and tailor treatment plans. Together, these studies represent significant advancements in clinical oncology, offering more refined diagnostic and therapeutic options that align with the principles of precision medicine.
GENETIC POLYMORPHISMS AND ESCC RISK IN CHINESE POPULATIONS
ESCC is a leading cause of cancer-related deaths globally, with particularly high incidence rates in East Asia, where it accounts for a significant proportion of cancer mortality[8,9]. Historically, environmental factors such as smoking, heavy alcohol consumption, and poor nutritional status have been identified as major risk factors for ESCC. However, the genetic landscape underlying ESCC susceptibility has only recently begun to be understood, thanks to advances in molecular genetics[10]. Understanding the genetic drivers of ESCC is crucial, as it can inform more personalized approaches to screening, prevention, and treatment. Lin et al[6] demonstrated that ACVR1C polymorphisms (rs4556933/rs77886248) modulate ESCC susceptibility in a Chinese Han cohort using a SNP detection method with 85% sensitivity and 78% specificity. While these metrics are clinically meaningful for the studied population, their cross-ethnic applicability is constrained by allele frequency disparities. In populations with divergent genetic architectures, reduced sensitivity could under detect true risk alleles (false negatives), particularly for low-frequency variants like rs77886248, thereby compromising risk prediction[11]. Conversely, lower specificity might inflate false positives in high-frequency SNP populations, leading to unnecessary screening. Crucially, the study’s observation of SNP-mediated risk inversion suggests that detection inaccuracies could fundamentally alter therapeutic decision-making - misclassifying protective alleles as pathogenic or vice versa. These limitations highlight the necessity for population-adjusted validation of SNP detection platforms to ensure equitable implementation of precision prevention strategies globally. Stratified analysis identified a critical gene-environment interaction: Older male smokers with the rs77886248 T>A variant exhibited 2.15-fold increased ESCC risk (95% confidence interval: 1.67-2.78), demonstrating smoking’s capacity to potentiate genetic susceptibility[12]. Haplotype analysis revealed synergistic SNP combinations (e.g., rs4556933-rs77886248, P < 1 × 10-5) that amplify risk beyond single-variant effects, advancing polygenic risk prediction models for ESCC. These methodological approaches collectively address the multifactorial etiology of cancer through integrated genetic-environmental profiling[13].
When compared to previous research, such as the genome-wide association study conducted by Abnet et al[14], which identified several loci associated with ESCC but left their functional relevance largely unexplored, Lin et al[6] provide a more mechanistic explanation for how these polymorphisms influence ESCC susceptibility. The study elucidates the mechanistic involvement of the identified gene within the TGF-β signaling cascade, a critical pathway governing essential cellular processes including proliferation, differentiation, and programmed cell death. Notably, TGF-β functions as a multifunctional cytokine that maintains cellular homeostasis through context-dependent regulation of these biological functions. Pathological dysregulation of this signaling axis has been mechanistically linked to tumorigenesis across multiple malignancies, with emerging evidence particularly implicating TGF-β pathway aberrations in the pathogenesis of ESCC[15]. The ACVR1C-TGF-β axis identified in this study paves the way for therapeutic development, particularly for ESCC patients with rs77886248 variants. Emerging TGF-β inhibitors showing efficacy in other TGF-β-driven cancers could be repurposed for genetically stratified ESCC cohorts. Furthermore, the integration of SNP profiling with smoking status enables precision prevention strategies, allowing targeted surveillance of high-risk individuals (smokers with risk haplotypes) in endemic regions. This dual approach - bridging molecular pathogenesis with environmental risk modulation - represents a paradigm shift in managing ESCC’s complex etiology[15].
IMAGING FEATURES AS PREDICTIVE MARKERS IN PANCREATIC CANCER
These findings have the potential to address one of the most critical challenges in oncology[16]. Early detection is hampered by the aggressive nature of PDAC and the absence of early symptoms, making it essential to identify reliable diagnostic markers that can differentiate between pancreatic cancer subtypes[17]. Luo et al[6] systematically analyzed 500 pancreatic cancer patients through multimodal imaging (CT, MRI, and EUS), establishing clinico-radiological correlations with histopathological subtypes. The study’s findings underscore the diagnostic utility of these imaging techniques in distinguishing between pancreatic cancer subtypes. PDAC typically presents as a hypodense mass with poorly defined borders on CT, reflecting its highly invasive nature, while intraductal papillary mucinous neoplasms are characterized by cystic lesions with mural nodules. Neuroendocrine tumors, on the other hand, show hypervascular patterns on contrast-enhanced imaging, indicating their distinct vascular architecture. This detailed analysis enhances diagnostic precision by enabling clinicians to more accurately classify tumor types, which is crucial for guiding therapeutic decisions and predicting patient outcomes. Luo et al’s methodological strength[7] lies in their multiparametric imaging framework integrating CT, MRI, and EUS, enabling synergistic tumor characterization - notably advancing beyond prior investigations like Hille et al’s single-modality studies[18] with limited cohort sizes. This multimodal paradigm demonstrates diagnostic complementarity, systematically addressing the intrinsic limitations of individual imaging techniques in pancreatic cancer evaluation.
Moreover, this study has significant implications for clinical practice. By identifying key imaging markers associated with aggressive subtypes like PDAC, it may enable earlier interventions, such as surgery or chemotherapy, which are crucial in improving survival rates. The integration of imaging data with molecular and genetic profiling could also pave the way for more personalized treatment approaches, such as targeted therapies or immunotherapies, which are currently under investigation for PDAC and other pancreatic cancers[19,20]. Given the poor outcomes associated with late-stage diagnosis, the ability to detect pancreatic cancer subtypes early through precise imaging techniques is a critical step forward in oncology. In conclusion, Luo et al’s study[7] provides a robust framework for enhancing the accuracy of pancreatic cancer diagnosis through multi-modality imaging. Their work contributes to the ongoing efforts to improve patient outcomes by facilitating earlier detection and personalized treatment strategies, particularly for aggressive cancers like PDAC. This study highlights the importance of integrating advanced imaging with molecular diagnostics to refine the approach to pancreatic cancer management.
CLINICAL IMPLICATIONS AND FUTURE DIRECTIONS
Integrating genetic and imaging data requires overcoming technical and logistical barriers. For example, developing interoperable databases to link SNP profiles with radiomic features could enable artificial intelligence-driven predictive models[21]. Both studies by Lin et al[6] and Luo et al[7] carry significant clinical implications for oncology practice. Lin et al’s identification[6] of ACVR1C polymorphisms in ESCC provides a critical step toward the development of genetic screening tools. Such tools could be invaluable for identifying individuals at higher risk, particularly in East Asian populations where ESCC incidence is notably higher. Additionally, combining ACVR1C screening with smoking cessation programs could synergistically reduce ESCC risk, while integrating liquid biopsies with imaging may enhance early PDAC detection[22,23]. This personalized prevention approach mirrors efforts in other cancers, such as breast cancer, where BRCA1/2 genetic screening has been transformative in identifying high-risk populations and offering preventive options[24,25].
In a similar vein, Luo et al’s work[7] on imaging technologies highlights the potential of refining diagnostic tools to better distinguish pancreatic cancer subtypes, including PDAC. Early detection of PDAC remains one of the greatest challenges in oncology, given its often asymptomatic nature until advanced stages[26]. By integrating multiple imaging modalities, such as CT, MRI, and EUS, clinicians can better characterize tumor biology and make more informed treatment decisions. As imaging techniques continue to evolve, combining them with genetic and molecular data - such as those provided by liquid biopsies - could revolutionize early detection, improve treatment outcomes, and minimize the need for invasive diagnostic procedures. This multidisciplinary approach is essential for addressing cancers with poor prognoses, such as PDAC and ESCC, where early detection is crucial for improving survival rates[27].
CONCLUSION
Lin et al[6] and Luo et al[7] exemplify the power of precision oncology. Future efforts should focus on validating these findings in diverse populations, optimizing cost-effective technologies, and fostering interdisciplinary collaboration to bridge genetic and imaging innovations. By addressing these challenges, the integration of molecular and imaging tools will transform cancer care, offering hope for historically intractable malignancies.
ACKNOWLEDGEMENTS
We are very grateful to Lin et al and Luo et al for their high-quality studies.
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade B, Grade C, Grade C
Novelty: Grade A, Grade B, Grade B, Grade B, Grade B
Creativity or Innovation: Grade A, Grade A, Grade B, Grade C, Grade C
Scientific Significance: Grade A, Grade B, Grade B, Grade B, Grade B
P-Reviewer: Chen ZJ; Li QJ; Tang YX S-Editor: Wei YF L-Editor: A P-Editor: Zhao S
