Bioinformatics Data Mining: Is the Pipette Mightier than the Scalpel?

The paper “COL11A1 was involved in cell proliferation, apoptosis and migration in non-small cell lung cancer cells” examines the role of collagen 11A1 or type XI collagen (COL11A1), which is mainly expressed by chondrocytes and mesenchymal stem cells, in addition to osteoblasts [1]. More interesting is the fact that this gene has been shown to be a differentiating factor between human carcinomas and benign pathologies of various organs (liver, pancreas, breast, lung, esophagus, and bowel among others), where inflammation is the predominant underlying process [2–4]. In addition, it has also been shown to play a role in chemoresistance of certain types of ovarian cancer [5]. The significance of this potential marker lies in its ability to not only signal the presence of a malignancy, but also in the fact that it’s presence correlates with the aggressiveness of the malignancy and the potential for lymphatic and metastatic spread.

Tu et al. [1] in this paper for the first time show that COL11A1 may also be involved in the pathogenesis of lung cancer. More importantly, using an elegant experimental model with COL11A1 knockdown in lung cancer cells, they were able to delve further into the mechanisms involved, thus showing the role of type XI collagen in cell proliferation and apoptosis. The diagnostic and therapeutic implications of these findings are apparent, as what could be a critical target for immunotherapy for an aggressive type of cancer is potentially revealed.

The real benefit…

In addition to the actual findings, the paper by Tu et al., also serves to underscore the importance of databases for aberrant expression prediction, such as the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas database [1, 6]. Data science or the science of “Big Data” has met increasing popularity given the significant commercial potential in several social, commercial and scientific fields. The common bond between these very different areas has been the use of computational approaches to explore possibilities, understand occurrences and, most important, predict future trends or behavior [7]. Biological disciplines could not be an exception and this led to the emergence of genomics in the 1980s as the combination of bid data, statistics and genetics. Over the last few decades this has evolved with an exponential rate and with the addition of increased computational power and techniques has led us to a brave new world, that of artificial intelligence (AI). This can certainly be the beginning of a new era in medicine, whereby diseases are not cured by surgery or other invasive or painful procedures, but rather by precise, patient-specific interventions which are the result of information accumulated and patterns recognized. An early example of this is the use of immunotherapy for cancer, which although at an early stage, has shown great potential.

However, as in everything in life, the enthusiasm of future progress and scientific revolution should be tempered by cautiousness. Specifically, when discussing the issue of large scale databases and “big data”, there are certain considerations, such as the volume of available data, the speed of generating and the speed of processing of the data, the changes in network science or algorithm development, and the balance between data sharing openness and personalized data privacy.

Overall, this paper serves as an example of how significant findings can result from the careful and judicious use of “big data”. Apart from its significant scientific findings of the potential role of COL11A1 in the diagnosis and therapy for non-small cell lung cancer, through an elegant experimental model it shows us the importance of integrating genomics into clinical practice.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of thepaper.