Urine miRNA signature as potential non-invasive diagnostic biomarker for Hirschsprung’s disease

1 Introduction

Hirschsprung’s disease (HSCR) (incidence 1/5,000 births) is a congenital gastrointestinal disorder caused by aganglionosis of the distal colon, which in newborns causes impaired defecation, constipation, and intestinal obstruction due to a lack of relaxation (Heuckeroth, 2018). The latter can impact either only short segments, the whole colon, and in few cases the whole gut (Heuckeroth, 2018). Currently, the international evidence-based treatment is the surgical removal of the aganglionic bowel. However, after undergoing surgical resection patients may have a restricted quality of life, i.e., due to recurrent enterocolitis (Menezes and Puri, 2006), partial incontinence or persisting defecation problems (Calkins, 2018).

Although HSCR anatomy is well described, individual phenotypes are complex, often making the diagnosis difficult (Heuckeroth, 2018). The most reliable diagnosis method for HSCR diagnosis include methods such as Rectal Suction Biopsies (RSB) that include the submucous layer (Lewis et al., 2003; de Lorijn et al., 2006; Romero et al., 2024). Usually, RSBs are combined with acetylcholinesterase (AChE) staining. This habitually requires high quality thick tissue and several biopsies in order to confirm the absence of ganglion cells, which sometimes leads to clinical complications (Friedmacher and Puri, 2015; Neeser et al., 2024). Nonetheless, it is difficult to decipher the functionality of potential immature ganglia in newborns, where an experienced pathologist together with a good clinical team could be required for a correct, unambiguous HSCR diagnosis. Consequently, the complexity and potential human errors can further exacerbate the risk of erroneous diagnoses in complicated cases (Shayan et al., 2004; Erbersdobler, 2024).

Recent studies have attempted to improve RBSs-AChE’s diagnosis precision. Methods such as software-based quantification of the AChE-stained cholinergic hyperinnervation (Braun et al., 2024), or measuring neural fiber trunk diameter for precisely establishing the length of the aganglionic segment in patients (Talebi et al., 2024) have been described. Yet, the requirement of good quality RBSs for the AChE staining continues to remain the key-challenge limiting their applicability.

Calretinin IHC has emerged as an alternative, promising form of staining. It presents several advantages in comparison to AChE histochemistry, like the possibility to perform the test on paraffin-embedded tissue sections (AChE requires cryosections), a straightforward staining pattern of clear binary interpretation (negative or positive), cost-effectiveness, and being performable regardless of patient age (Muller et al., 2015; Romero et al., 2024). This can provide an earlier HSCR diagnosis than AChE, and help to avoid repeated RSBs. This would allow patients to undergo surgery in the first few months of life, thereby reducing the number of enterostomies and posterior complications (Romero et al., 2024). Nevertheless, high quality RSBs are still needed, and many can be inconclusive even using Calretinin staining (Korsager et al., 2023).

Despite their advantages, all these tests need highly specialized equipment, radiation exposure, additional hospital time and are challenging to perform in neonates (Osatakul et al., 1999; Diamond et al., 2007). Given the importance of an early diagnosis, there is thus a room for novel diagnostic techniques that are non-invasive, are relatively accessible to perform, and are easy to interpret, thus allowing to decide whether further biopsies are necessary or could be avoided.

Posttranscriptional regulation by microRNAs (miRNAs), small non-coding RNA molecules of 19–25 nucleotides, which constrain the expression of target genes by directly binding to their mRNAs, is an important regulatory mechanism of gene expression (Hosako et al., 2009). Usually, miRNAs are secreted from most cell types inside exosomal extracellular vesicles, which keep miRNA sequences stable (Mall et al., 2013). These vesicles and miRNAs can later be specifically detected in body fluids such as urine, blood, cerebrospinal fluid or saliva (Kroh et al., 2010; O’Brien et al., 2018; Salehi and Sharifi, 2018).

Given their ubiquitous presence and their associations with health and disease, in recent years, miRNAs have become popular as biomarkers, which refer to biological, objective markers representative of certain healthy or diseased states (Wang et al., 2016; Condrat et al., 2020). Some of the first published studies testing the diagnostic utility of miRNA in cancer were already published in around 2008 (Lawrie et al., 2008; Mitchell et al., 2008). Both studies used circulating miRNA in serum or plasma, which was a relatively non-invasive and feasible technique. Today there are multiple published studies that report the role of circulating miRNA as a diagnostic marker across several different types of cancers (Chen et al., 2008; Liu et al., 2012; Bertoli et al., 2015; Subramani et al., 2015; Fabris et al., 2016), cardiovascular diseases (Zhou et al., 2018), sepsis (Benz et al., 2016), gestational diabetes mellitus (Zhao et al., 2011), Parkinson’s disease (Gries et al., 2021) and several other diseases.

Nevertheless, it was the presence of specific urine miRNAs described in other diseases, like nephrotic syndrome (Luo et al., 2013), prostate cancer (Korzeniewski et al., 2015) or to determine exposure to pesticides (Weldon et al., 2016), that led to our hypothesis that similar specific miRNA patterns probably also exist in urine of HSCR patients. Indeed, some studies have already identified dysregulated miRNA expression in intestinal tissue (Shen et al., 2016; Zhang et al., 2024) or serum (Tang et al., 2014) from HSCR patients.

However, bearing in mind the age of HSCR patients, we designed this “proof of concept” study in urine with the aim of identifying a non-invasive biomarker to complement the histology and clinical diagnosis in the future.

2 Materials and methods 2.1 Patient samples

The collection and use of patient material was performed according to informed consent signed by patients’ parents and approved by the “Medizinische Ethik-Kommission II” of the Medical Faculty Mannheim, University of Heidelberg (2011-237 N-MA). Samples were only identified by sequential code numbers with no other identifying details.

Urine from 5 patients diagnosed with HSCR and 5 healthy controls was collected “clean-catched” and stored at −80°C until analysis. The characteristics of the patient’s cohort is described in Supplementary Table S1.

2.2 miRNA extraction from urine

miRNA was obtained using the extracellular vesicle extraction process. Urine samples were pre clarified by spinning for 15 min. at 3000 rpm. To isolate the extracellular vesicles, the supernatants were again ultra-centrifuged for 1 h at 28,000 g. Following this, the supernatant was removed, and the pellet was resuspended in TRIzol™ Reagent (Invitrogen, Thermofisher Scientific Inc., Waltham, MA, United States) to isolate RNA according to manufacturer’s protocol. Following this, the mirVana™ miRNA Isolation Kit (Invitrogen, Thermofisher Scientific Inc., Waltham, MA, United States) was utilized for the rest of the miRNA purification, also according to manufacturer’s protocol.

The miRNA concentration was measured with the infinite M200 micro plate reader (Tecan, Mainz-Kastel, Germany) and the miRNA quality was tested with the RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, United States) and the Agilent Bioanalyzer 2,100 (Agilent Technologies Inc., Santa Clara, CA, United States).

2.3 miRNA microarrays

miRNA expression profiling was performed using the GeneChip™ miRNA 4.0 Arrays (Thermo Fisher Scientific Inc., Waltham, MA, United States) with the help of our NGS Core Facility (Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany). Biotinylated antisense cDNA was prepared according to the standard labeling protocol with the GeneChip® WT Plus Reagent Kit and the GeneChip® Hybridization, Wash and Stain Kit (both from Thermo Fisher Scientific Inc., Waltham, MA, United States). Afterwards, the hybridization on the chip was performed on a GeneChip Hybridization oven 640, then dyed in the GeneChip Fluidics Station 450 and thereafter scanned with a GeneChip Scanner 3,000.

Data was analyzed using a commercial software package SAS JMP15 Genomics. The raw fluorescence intensity values were normalized applying quantile normalization and RMA background correction. Thereafter, One Way ANOVA was performed to identify differential expressed genes. miRNAs with an adjusted p-value ≤0.05 were considered as significant.

The raw and normalized data are deposited in the Gene Expression Omnibus database (accession No. GSE277874).

2.4 miRNA selection and database target search

Data was sorted through, and 10 miRNAs (p-value ≤0.05) were selected based on their expression fold: the 5 that were most upregulated, and the 5 that were most down-regulated.

Based on this, the corresponding targets of each miRNA were searched using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/) (Sticht et al., 2018). First, the putative target list of each of the 10 selected miRNAs was downloaded, where the first top 10 targeted genes were listed. The resulting miRWalk list was cross-referenced with a second database, miRDB (mirdb.org) (Chen and Wang, 2020).

Additionally, already described HSCR-related genes (BDNF, ECE1, EDNRB, EDN3 and ERBB3, GDNF, RET, NRG1, SOX10, etc.) were searched in both databases for all the miRNAs. Ultimately, other neuronal- related genes (f.i.: linked to neuronal migration, neuronal crest development, neurogenesis, etc.) were also browsed in the same databases.

2.5 RT-qPCR

cDNA synthesis was performed using the TaqMan™ MicroRNA Reverse Transcription Kit (Applied Biosystems Inc., Thermo Fisher Scientific Inc., Waltham, MA, United States) according to manufacturer’s instructions. The reaction was carried out using a peqSTAR Thermocycler (PeqLab Biotechnology GmbH, Erlangen, Germany) as follows: 5 min. Denaturation at 70°C, 10 min. Annealing at 20°C, 60 min. Elongation at 40°C, and 10 min. Inactivation at 70°C.

The Sensifast SYBR Low-ROX Kit (BIO-94020, Bioline, Meridian Biosciences, OH, United States) was used for the RT-qPCR following the manufacturer’s instructions. Briefly, amplification reactions were run using a Tm of 57°C, in the QuantStudio 5 device (Applied Biosystems Inc., Thermo Fisher Scientific Inc., Waltham, MA, United States) as follows: Hold stage: Step 1: 2 min. at 50°C, Step 2 (denaturation): 10 min. at 95°C; PCR Stage (40 cycles): Step 1 (denaturation): 15 s at 95°C, Step 2 (annealing): 1 min. at 57°C; followed by a final Melting Curve Stage: Step 1: 15 s at 95°C, Step 2: 1 min. at 57°C, Step 3 (Dissociation): 15 s at 95°C.

Primers’ sequences were purchased to OriGene Technologies GmbH, Herford, Germany. In total, 3 housekeeping control primers and 10 miRNA primers were used (see Supplementary Table S2).

The comparative 2−ΔΔCt method was used to calculate gene expression, where data were first normalized to the housekeeping standard (dCt: Target Ct—Housekeeping Ct). Then, for each gene sample ddCt (ddCt: Sample dCt—Calibrator dCt) was calculated using the average of the controls as a calibrator. Finally, fold 2−ΔΔCt was calculated for each miRNA.

2.6 Statistical analysis

Statistical analysis was performed using the One Way ANOVA method for the miRNA microarray data analysis. The F.N. Test was used to compare differences in miRNA expression between controls and HSCR patients in the RT-qPCR test. Differences were considered statistically significant at p-value ≤0.05.

3 Results 3.1 miRNA microarray analysis of HSCR urine

In an initial “proof of concept” study, urinary miRNA from 5 HSCR patients and 5 healthy controls was analyzed using the GeneChip™ miRNA 4.0 Arrays (Thermo Fisher Scientific Inc., Waltham, MA, United States). The original array data is available on the NCBI Gene Expression Omnibus (GEO) browser (Reference number GSE277874).

Despite the limited size of our studied cohort, we could find significant differences in the expression of some urinary miRNAs between controls and HSCR patients. Though still preliminary results, they may be used as a basis for further research. The heatmap in Figure 1 shows the results of the clustering analysis based on the normalized expression values of up- (red) and down-regulated (blue) miRNAs (Figure 1). The distribution of the miRNAs is represented in the Volcano plot (Figure 1).

Figure 1. Overview of microarray analysis. (A) Heatmap of relative miRNAs expression. Differential expression is shown between Controls and HSCR samples, color scale represents up- (red), no threshold (gray), or downregulation (blue). (B) Volcano plot showing miRNAs distribution after ANOVA analysis.

After statistical analysis, data was sorted out based in fold changes in order to select the miRNAs that were most dysregulated. Results showed hsa-miR-378 h, hsa-miR-210-5p, hsa-miR-6876-3p, hsa-miR-634 and hsa-miR-6883-3p as the most upregulated miRNAs; while hsa-miR-4443, hsa-miR-22-3p, hsa-miR-4732-5p, hsa-miR-3187-5p, and hsa-miR-371b-5p were the most downregulated miRNAs in urine of HSCR patients compared to urine of healthy controls (p-value ≤0.05) (see Table 1).

Table 1. List of the 10 selected most dysregulated miRNAs identified in HSCR urine.

3.2 Most dysregulated urine miRNAs in HSCR patients target genes associated to HSCR pathology

With the aim of verifying the potential role of those miRNAs in HSCR development, we performed a target analysis exploration with the help of miRWalk (Sticht et al., 2018) and miRDB (Chen and Wang, 2020) databases.

Initially, we investigated the top 10 targeted genes in both databases with the highest score (see Table 2). Despite the high diversity of genes targeted by the selected miRNAs, it was possible to already find genes like ALKBH5, which was related to enteric neuronal crest migration in HSCR (Wang et al., 2021), or genes linked to neuronal migration, like DAB2IP (Lee et al., 2012) or NF1 (Sanchez-Ortiz et al., 2014), as well as to neuronal motility, like GMR5 (Turunen et al., 2018), or to other neuronal processes such as BTG2 (el-Ghissassi et al., 2002) or KIRREL3 (Hisaoka et al., 2021).

Table 2. Target database analysis of the selected dysregulated miRNAS in HSCR urine.

Further analysis in the same databases revealed that most of the dysregulated miRNAs in HSCR urine identified in the microarray regulate genes strongly associated to HSCR (see Table 3). For instance, genes like EDNRB (Puffenberger et al., 1994), RET (Luo et al., 1993; Emison et al., 2005), BDNF (Schriemer et al., 2016), GDNF (Parisi and Kapur, 2000), SOX10 (Prasad et al., 2011) etc. are targets of many of our detected miRNAs. In addition, a wide spectrum of different genes related to HSCR (Parisi and Kapur, 2000; Heuckeroth and Schäfer, 2016; Luzón-Toro et al., 2020) such as EDN3, ECE-1, ERBB3 (Gershon, 2021), NRG1 (Gui et al., 2013), NTKR3 (Sanchez-Mejias et al., 2009) or L1CAM (Jackson et al., 2009), have been also found in the databases (Table 3). Aside from those, other genes participating in signaling processes, like MAPK8 (Kuil et al., 2021), TMP3 (Camilleri et al., 2019) or PIK3C2B (Fu et al., 2020) have also recently been connected to ENS formation and HSCR.

Table 3. Specific miRNA targets associated with HSCR or neuronal processes (neuronal migration, neurogenesis, etc.).

Following that, we also searched for other genes related to critical neuronal processes for a proper Enteric Nervous System (ENS) formation, like neurogenesis, neurodevelopment or neuronal migration (see Table 3). We found genes like ALKBH5 that could be critical for enteric neuronal crest migration in HSCR (Wang et al., 2021); in addition to others important for neuronal migration: as NF2 (Toledo et al., 2018), NEUROG2 (Heng et al., 2008) and NEUROD1 (Singh et al., 2022) or DAB2IP (Lee et al., 2012); as well as genes participating in neuronal proliferation NEGR1 (Kim et al., 2014), or in neuronal assembly NFASC (Zonta et al., 2008).

From all examined HSCR urinary downregulated miRNAs, miR-4732-5p was the one that was associated with the highest number of genes related to HSCR in both databases, 8 genes in miRWalk (BDNF, GDNF, SOX10, L1CAM, NRG1, ECE1, ERBB3, EDN3) together with 3 more genes in miRDB (RET, GDNF, EDN3); followed by hsa-miR-3187-5p with 5 genes in miRWalk (GDNF, L1CAM, NRG1, RET, ECE1) and 4 more in miRDB (GDNF, RET, BDNF, NRG1); and finally by hsa-miR-4443 with 5 genes in miRWalk (GDNF, NRG1, ERBB3, ECE1, SOX10) and 3 more in miRDB (PIK3C2B, NTRK3, ECE1) (Table 3).

Regarding the upregulated sequences, the miRNAs with more HSCR-associated gene targets were hsa-miR-210-5p with 5 genes in miRWalk (RET, L1CAM, GDNF, BDNF, SOX10) and 1 more in miRDB (SOX10); together with hsa-miR-6876-3p, also with 5 genes in miRWalk (ERBB3, GDNF, EDNRB, ECE1, BDNF) and one more located in miRDB (NRG1) (Table 3).

Altogether, the identified HSCR urinary-dysregulated miRNAs of interest targeted genes associated with HSCR pathology and also others related to neuronal processes that may be important also in ENS formation.

3.3 RT-qPCR validation of urinary miRNA expression

Lastly, we evaluated the expression of the above selected miRNAs (see Table 1), in the urine of the HSCR patients and controls by RT-qPCR.

Here we could verify an increased expression of hsa-miR-6883-3p and hsa-miR-6876-3p, as well as a decreased expression of hsa-miR-4732-5p (F.N Test p = 0.000015) and hsa-miR-3187-5p (F.N Test p = 0.000002) (Figure 2) in HSCR urine compared to the controls, which confirmed the previous results of those miRNAs in the microarrays.

Figure 2. (A) Overview of RT-qPCR analysis of the selected miRNAs in HSCR urine samples compared to controls: miR3187-5p (F.N Test p = 0.000002), miR4732-5p (F.N Test p = 0.000015), miR6876-3p, miR-6883-3p. (B) Fold change comparison of 2 − ∆∆Ct from HSCR to control samples (HSCR/Controls).

4 Discussion

Our study demonstrates that specific dysregulated miRNAs can be detected in the urine of HSCR patients when compared to healthy controls. In our HSCR patients’ cohort, we could identify several upregulated and downregulated miRNAs by microarray analysis that are, related to key functional aspects in ENS development. Analyzing the predicted targets of the top 5 up and downregulated miRNAs in data bases, we could verify that they do regulate genes related to HSCR pathology, and also to neuronal processes that are maybe necessary for the proper development of the Enteric Nervous System (ENS) formation, like neurogenesis, neurodevelopment or neuronal migration (Table 3).

When we compare our results with other miRNA studies in HSCR, our identified miRNAs in HSCR urine differ from those found by others in HSCR serum (Tang et al., 2014), HSCR intestine (Shen et al., 2016; Zhang et al., 2024), or in developing ENS cells (Pai et al., 2023). However, the miRNAs found by these studies are also different between each other, possibly because of differential expression between tissue and body fluids and the specific cohorts studied.

Some of the selected miRNAs found in HSCR urine regulate RELA and ERBB3 genes according to the search in miRNA databases. We recently published a downregulated expression of ERBB3 and RELA mRNAs in distal aganglionic segments of HSCR patients in a cohort of 25 patients (Elkrewi et al., 2024). ERBB3, a member of the EGF receptor tyrosine kinase family, is known to play an important role in neural crest development (Prasad et al., 2011), while RELA, the main subunit of the NF-κB pathway (Perkins, 2007), participates in embryonic neurogenesis and neural progenitor migration and differentiation (Mémet, 2006; Zhang and Hu, 2012). Therefore, it could be interesting to further validate the miRNA targets the miRNA targets at protein level in tissue or blood HSCR samples in the future.

Regarding the potential causes of miRNA dysregulation in HSCR, miRNAs expression can be regulated at transcriptional (changes in gene expression) or post-transcriptional (changes in miRNA processing) levels (Gulyaeva and Kushlinskiy, 2016). Prospective investigation is needed to assess if already known HSCR mutations mutations, or other HSCR disease’s characteristics, i.e., alterations in intestinal tissue, are somehow responsible for these miRNA modifications.

In addition, cellular pathways (Treiber et al., 2019) and various physiological and pathological stimuli, such as hormones, stress, inflammatory cytokines, DNA methylation status, etc. have been related to affect miRNA expression (Gulyaeva and Kushlinskiy, 2016; Juźwik et al., 2019). Of these, processes like inflammation, and neurodegeneration are certainly consistent with HSCR pathology and could be considered as possible miRNA dysregulation mechanisms for future investigation.

Given that our chosen list of dysregulated miRNAs in HSCR urine regulate the expression of genes related to HSCR and to neuronal and cell migration processes (see Table 3), we think they may be good candidates for HSCR biomarkers. In particular, sequences like miR-4732-5p, hsa-miR-3187-5p and hsa-miR-6876-3p target several HSCR-related genes and were validated in our RT-qPCR analysis. As an example, high levels of hsa-miR-6876-3p, which can target GDNF, ERBB3, EDNRB and BDNF mRNAs, will lead to a lower expression of those genes and thus impair neurogenesis and neuronal migration, which finally may impact the proper gut innervation in those patients.

Importantly, the expression of specific candidate miRNAs can also be detected using nanophotonic biosensors integrated into portable lab-on-a-chip platforms, which are applicable in clinical and environmental diagnostics (Tarasov et al., 2016; Smith et al., 2017; Rebelo et al., 2019; García-Chamé et al., 2020). Thus, when at some point a defined miRNA pattern would be determined, the miRNA detection could easily be implemented in a microanalytical chip system and thereafter be used routinely to complete HSCR diagnosis, potentially helping future patients.

In summary, this work provides initial results that are promising in the search of a new complementary diagnosis method for HSCR. They show they show that indeed some miRNAs have dysregulated expression as predicted. However, further research is still necessary to validate a specific miRNA signature in a larger cohort of patients that could be used as HSCR biomarker in the future. After validation, these can be used for the creation of a non-invasive, easy to interpret and relatively simple novel diagnostic tool, complementary to histology, patient history, and pathology.

This preliminary study serves as a foundation for future investigation. A larger number of patients are required to increase the robustness of the results. Ideally, patients may be recruited from a network of hospitals at national level. Once verified, selected miRNAs must be correlated with clinical history, histology and surgery outcome. Based on that, the best miRNAs may be initially added to other diagnosis approaches until their application is fully demonstrated, and finally be implemented in a regular manner.

5 Conclusion

HSCR patients present a detectable miRNA dysregulated signature in urine that could work as an easier, non-invasive and more affordable method to complement current Hirschsprung’s disease diagnosis tests. Therefore, further research is necessary to validate this preliminary results in a larger cohort of patients.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, GSE277874.

Ethics statement

The studies involving humans were approved by the “Medizinische Ethik-Kommission II” of the Medical Faculty Mannheim, University of Heidelberg (2011-237N-MA). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

AS: Data curation, Formal analysis, Investigation, Visualization, Writing – original draft. RK: Investigation, Resources, Writing – review & editing. EE: Investigation, Writing – review & editing. CT: Data curation, Formal analysis, Writing – review & editing. JF: Resources, Writing – review & editing. AA: Resources, Writing – review & editing. RM: Resources, Writing – review & editing. NAH-C: Resources, Writing – review & editing. MB: Funding acquisition, Resources, Writing – review & editing. LW: Funding acquisition, Resources, Writing – review & editing. K-HS: Funding acquisition, Resources, Writing – review & editing. MAT-L: Conceptualization, Data curation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The work of Abhijit Sreepada and Enas Zoheer Elkrewi was supported through funding from the Medical Faculty Mannheim, University of Heidelberg, under the auspices of the Master’s program “Translational Medical Research” (TMR). Note: This Master’s program has no funding registration code for their students’ practicum. For the publication fee we acknowledged financial support by Heidelberg University.

Acknowledgments

We would like to thank Camela Jost (Manager of General Core Equipment unite, Medical Faculty of Mannheim, University of Heidelberg) for her kind support and technical help.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2024.1504424/full#supplementary-material

Footnotes References

Bertoli, G., Cava, C., and Castiglioni, I. (2015). MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 5, 1122–1143. doi: 10.7150/thno.11543

PubMed Abstract | Crossref Full Text | Google Scholar

Braun, Y., Friedmacher, F., Theilen, T., Fiegel, H. C., Weber, K., Harter, P. N., et al. (2024). Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence. J. Pediatr. Gastroenterol. Nutr. 79, 729–737. doi: 10.1002/jpn3.12339

PubMed Abstract | Crossref Full Text | Google Scholar

Calkins, C. (2018). Hirschsprung disease beyond infancy. Clin. Colon Rectal Surg. 31, 051–060. doi: 10.1055/s-0037-1604034

Crossref Full Text | Google Scholar

Camilleri, M., Wieben, E., Eckert, D., Carlson, P., Hurley O’Dwyer, R., Gibbons, D., et al. (2019). Familial chronic megacolon presenting in childhood or adulthood: seeking the presumed gene association. Neurogastroenterol. Motil. 31:e13550. doi: 10.1111/nmo.13550

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, X., Ba, Y., Ma, L., Cai, X., Yin, Y., Wang, K., et al. (2008). Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18, 997–1006. doi: 10.1038/cr.2008.282