Gut microbiota could be an indicator of early-stage lung cancer

Often diagnosed at an advanced stage, lung cancer is associated to a high mortality rate. An earlier diagnosis would greatly improve both care and chances of survival. Can gut dysbioses serve as an indicator of lung cancer, as for many other diseases, including other types of cancer?

Specific gut dysbiosis signature as an indicator of the stage of cancer

The gut microbiota of 42 patients with three different types (sidenote: Adenocarcinoma (37 patients), squamous cell carcinoma (3 patients), large-cell carcinoma (2 patients) )  of non-small-cell lung cancer (NSCLC) at an early stage (metastasis had taken place in only 3 patients), and that of 65 healthy control subjects, were analyzed using 16S rRNA sequencing. Intestinal dysbioses were observed in the patients with lung cancer: increased presence of species belonging to the Ruminococcus genus and the Lachnospiraceae and Enterobacteriaceae families, among others, compared to control subjects. Therefore, the composition of the microbiota may change as lung cancer develops. In addition, the composition of the intestinal microbiota was specific of each stage of the cancer, with some bacteria only present in the three patients displaying metastasis.

A diagnostic tool?

In order to develop a non-invasive diagnostic tool for early-stage lung cancer, 13 biomarkers based on OTUs were identified. Together, these biomarkers made it possible to accurately predict the presence of lung cancer (97.6% of cases). This model was confirmed in a second cohort (34 patients and 40 control subjects), with its predictive power remaining high (76.4%), although lower than in the initial cohort. From this model it was possible to construct a “patient discrimination index” to identify patients with early-stage lung cancer. Based on a weighted score, the index is easy to use for clinical purposes. Its predictive power in the initial cohort (92.4%) was also higher than that measured in the validation cohort (67.7%). Larger cohorts could improve the model and its predictive power.