Researchers from Sechenov University, affiliated with the Russian Ministry of Health, collaborated with both Russian and international colleagues to systematically assess how tumors influence adjacent normal tissues. Their work reveals that tissue surrounding a cancer lesion should not be treated as entirely normal and, consequently, should not be used as a control sample in studies. This finding marks a turning point in how researchers approach tumor biology and the interpretation of transcriptomic data in oncology.
The team notes that the neighborhood around a tumor has long been presumed normal because, from a morphological standpoint, there are no clear cancer cells visible in the surrounding tissue. Yet the researchers found that when the inquiry is broadened to the level of gene activity, the picture changes dramatically. In their view, tumor-adjacent tissue exhibits altered gene regulation patterns that reflect the systemic impact of cancer, rather than a truly healthy state. Such shifts in gene expression indicate that proximity to the tumor influences cellular behavior in ways that conventional histology cannot detect. This has important implications for how scientists assemble control samples and interpret molecular data in cancer research.
To evaluate these regulatory changes, the investigators compared gene expression profiles between tumor tissue and nearby non-tumor tissue. Ideally, samples from tissue located far from the tumor would serve as pristine controls. In practice, however, researchers frequently rely on adjacent tissue because it is readily available in clinical specimens. The study highlights that this common practice may introduce confounding factors, potentially distorting conclusions about the molecular differences that drive cancer progression and response to therapy.
In this first systematic examination of how tumors affect neighboring tissues across multiple cancer types, the researchers analyzed nearly 5,000 gene expression profiles. The breadth of the data allowed for robust identification of consistent patterns that emerge in the tumor microenvironment and in the immediately adjacent non-tumor tissue. Across samples, several systemic differences emerged at the molecular level. These include changes linked to inflammatory signaling and immune cell activation, disruptions in intracellular transport, shifts in cellular respiration, and remodeling of the extracellular matrix. Importantly, many of these patterns were present not only within tumor lesions but also in the neighboring tissue, underscoring the influence of the tumor on the surrounding cellular milieu.
The team demonstrated that the observed molecular alterations are attributable to the tumor’s presence, rather than representing intrinsic abnormalities of the adjacent tissue. The convergence of these changes in both cancerous cores and their neighboring zones supports the conclusion that tumors exert a broad, local effect on gene regulation. This insight helps explain why adjacent tissue often fails to behave like truly normal tissue in experimental and clinical contexts.
With these discoveries, researchers gain a more accurate framework for interpreting transcriptomic analyses used in both basic research and personalized cancer treatment. The evidence suggests that analysts should account for the tumor’s influence when selecting control tissues and when interpreting patterns of gene expression that inform prognosis and therapeutic decisions. The methodological refinement promises to enhance the precision of molecular studies, enabling more reliable identification of cancer-specific signatures and more informed assessment of potential treatment targets in individual patients.
Looking ahead, the findings encourage the development of standardized approaches that recognize and adjust for the tumor’s impact on nearby tissues. Such practices could improve cross-study comparability and foster more nuanced models of tumor biology. In short, the research shifts the paradigm: adjacency to a tumor is not a perfectly normal reference state, and acknowledging this fact is essential for accurate molecular interpretation and the advancement of personalized oncology.