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Abstract Background Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models. Results As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra. Conclusions Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

Abstract Background Different risk-based colorectal cancer (CRC) screening strategies, such as the use of polygenic risk scores (PRS), have been evaluated to improve effectiveness of these programs. However, few studies have previously assessed its usefulness in a fecal immunochemical test (FIT)-based screening study. Methods A PRS of 133 single nucleotide polymorphisms was assessed for 3619 participants: population controls, screening controls, low-risk lesions (LRL), intermediate-risk (IRL), high-risk (HRL), CRC screening program cases, and clinically diagnosed CRC cases. The PRS was compared between the subset of cases (n = 648; IRL+HRL+CRC) and controls (n = 956; controls+LRL) recruited within a FIT-based screening program. Positive predictive values (PPV), negative predictive values (NPV), and the area under the receiver operating characteristic curve (aROC) were estimated using cross-validation. Results The overall PRS range was 110–156. PRS values increased along the CRC tumorigenesis pathway (Mann-Kendall P value 0.007). Within the screening subset, the PRS ranged 110-151 and was associated with higher risk-lesions and CRC risk (ORD10vsD1 1.92, 95% CI 1.22–3.03). The cross-validated aROC of the PRS for cases and controls was 0.56 (95% CI 0.53–0.59). Discrimination was equal when restricted to positive FIT (aROC 0.56), but lower among negative FIT (aROC 0.55). The overall PPV among positive FIT was 0.48. PPV were dependent on the number of risk alleles for positive FIT (PPVp10-p90 0.48–0.57). Conclusions PRS plays an important role along the CRC tumorigenesis pathway; however, in practice, its utility to stratify the general population or as a second test after a FIT positive result is still doubtful. Currently, PRS is not able to safely stratify the general population since the improvement on PPV values is scarce.