Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Novel targets in rectal cancer by considering lncRNA–miRNA–mRNA network in response to Lactobacillus acidophilus consumption: a randomized clinical trial

Abstract

We aimed to explore the lncRNA–miR–mRNA network in response to Lactobacillus acidophilus (L. acidophilus) consumption in rectal cancer patients. The candidate miRs were first taken from the GEO and TCGA databases. We constructed the lncRNA–miR–mRNA network using the high-throughput sequencing data. At last, we created a heatmap based on the experimental data to show the possible correlation of the selected targets. The expression levels of selected targets were measured in the samples of 107 rectal cancer patients undergoing placebo and probiotic consumption and 10 noncancerous subjects using Real-Time PCR. Our analysis revealed a group of differentially expressed 12 miRs and 11 lncRNAs, and 12 genes in rectal cancer patients. A significant expression increase of the selected tumor suppressor miRs, lncRNAs, and genes and a substantial expression decrease of the selected oncomiRs, onco-lncRNAs, and oncogenes were obtained after the probiotic consumption compared to the placebo group. There is a strong correlation between some network components, including miR-133b and IGF1 gene, miR-548ac and MSH2 gene, and miR-21 and SMAD4 gene. In rectal cancer patients, L. acidophilus consumption was associated with improved expression of the lncRNA–miR–mRNA network, which may provide novel monitoring and therapeutic approaches.

Introduction

Colorectal cancer (CRC) is the third most common cancer, ranked as the second cause of cancer-related death with 9.4% of the total cancer deaths1. Accordingly, there is a complex association between gut microflora, cancer development, and treatment response of CRC2,3. Therefore, interventions targeting the gut microbiome can offer a clinical application for cancer prevention and treatment. The cross-talk between the gut microbiome and host is mediated by metabolites, proteins, and non-coding RNAs4,5. Recent studies suggested that the gut microbiota can influence the miRs' expression pattern, leading to intestinal homeostasis4,6. On the other hand, the microRNAs (miRs) can shape the gut microbiome5,7. However, the expression of non-coding RNAs can be modulated by other factors such as diet.

The mechanisms by which some dietary factors modify non-coding RNAs' expression, including miRs and long non-coding RNAs (lncRNA), can lead to the modulation of the gut microbiota and the inhibition of tumor growth8. Yuan et al. presented the integrated expression analysis of miRs and intestinal microbiome profiles in CRC patients9. Their findings enlightened the highly interconnected network between miRs and microbiome composition and supported the miRs' role in mediating host-microbial interaction in rectal cancer9. In the previous study, we demonstrated that the consumption of the probiotics, such as Lactobacillus acidophilus (L. acidophilus) and Bifidobacterium bifidum, could increase the expression of the tumor suppressor miRs and decrease the oncogenes and their target genes in an animal model of colon cancer10. Likewise, Rodríguez-Nogales et al. reported that probiotic consumption could improve the expression of miR-155 and miR-223 in an animal model of colitis11. Similarly, Gianotti et al. showed that the low dose administration of L. acidophilus could improve health status and the immune system function in CRC patients12.

It has been comprehended that lncRNAs could interact with miRs and might regulate their target gene expression. This phenomenon has been known as the sponge-like effect of lncRNAs and is explained in the competitive endogenous RNAs (ceRNA) hypothesis. The ceRNA networks have revealed a new mechanism of interactions between RNAs and play fundamental roles in several biological processes and the progress of neoplasms. They might serve as diagnostic and prognosis biomarkers and even therapeutic targets.

In this setting, lncRNAs can bind to protein-encoding gene sequences to form triple RNA–DNA complexes, suppress gene expression, interact with proteins, and develop nucleic acid–protein interactions9. A complex correlation between coding and non-coding RNAs has been observed in different malignancies, including CRC13,14. Therefore, it is vital to investigate the interactions and mechanisms in regulatory networks, including lncRNAs, miRs, mRNAs, genetic mutations, and epigenetic modifications in rectal cancer. We believe that the new biomarkers will be revealed to diagnose and develop new treatment modalities for rectal cancer. Consequently, we aimed to investigate the profile of the lncRNA–miR–mRNA network in response to L. acidophilic consumption in patients with non-metastatic rectal cancer.

Results

Identification of differentially expressed miRs, lncRNAs, and mRNAs

GEO and TCGA datasets were analyzed to identify differentially expressed miRs, lncRNAs, and genes in colorectal cancer and normal samples. FunRich_3.1.3 software made a Venn diagram and extracted the common to the selected datasets (Fig. 1). A total of 80 miRs (33 up-regulation and 47 down-regulation) (Fig. 1A), 293 mRNAs (100 up-regulation and 193 down-regulation) (Fig. 1B), and 170 lncRNAs (111 up-regulation and 39 down-regulation) (Fig. 1C) were obtained from the selected datasets. The top up-regulated miRs were miR-21, miR-20a, and miR-20b, and the top down-regulated miRs were miR-34a, miR-424, and miR-378a (Table 1). The target genes of selected miRs have been represented in Tables 2, 3, and 4. Likewise, the lncRNAs of selected miRs were obtained using the LncRNADisease, Lnc2Cancer v3.0, LncRNA2target, and TANRIC datasets (Table 5).

Figure 1
figure 1

Venn diagram of the differently expressed miRs, lncRNAs, and mRNAs between GEO and TCGA datasets. Allocation of (A) the 80 differently expressed miRs (33 up-regulation and 47 down-regulation), (B) the 293 differently expressed genes (100 up-regulation and 193 down-regulation), and (C) the 170 differently expressed lncRNAs (111 up-regulation and 39 down-regulation) found between the selected datasets used in the present study.

Table 1 The predicted candidate miRs in rectal cancer patients.
Table 2 The predicted candidate genes in rectal cancer patients.
Table 3 Interaction analysis between the candidate miRs and target genes in rectal cancer patients.
Table 4 The candidate genes in rectal cancer patients.
Table 5 The predicted candidate lncRNAs in rectal cancer patients.

Enrichment analysis of differentially expressed genes (DEGs)

To examine the biological functions of the 293 DEGs, GO analysis was performed in the FunRich software. The up-regulated DEGs were enriched in the receptor binding, protein serine/threonine kinase activity, and growth factor activity (Table 6). In contrast, down-regulated DEGs' functional enrichment terms were mainly correlated with the transcription factor activity, kinase regulator activity, DNA binding, and DNA repair protein (Table 7). Up-regulated DEGs were enriched in the pathways, including the IFN-γ pathway, the Glypican pathway, and the TNF receptor signaling pathway (Fig. 2A). However, down-regulated DEGs pathways were enriched, including the IFN-γ pathway, IGF1 pathway, P53 pathway, and TNF receptor signaling pathway (Fig. 2B).

Table 6 GO (Gene Ontology) enrichment analysis for up-regulated DEGs.
Table 7 GO (Gene Ontology) enrichment analysis for down-regulated DEGs.
Figure 2 
figure 2

KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of the DEGs. (A) Top 9 functional network/pathways associated with these up-regulated DEGs through KEGG analysis with a p-value of less than 0.05. (B) Top 9 functional network/pathways related to these down-regulated DEGs through KEGG analysis with a p-value of less than 0.05. Permission has been obtained from Kanehisa laboratories for using the KEGG pathway database47.

Protein–protein interaction (PPI) network analysis of DEGs

PPI analysis of the 293 DEGs was performed in the FunRich software (score ≥ 7). TP53, SP1, CTNNB1, ESR1, and GSK3B were hub nodes with higher node degrees in up-regulated genes (Fig. 3A). SMAD4, MAPK11, MYC, TGFBR2, GRB10, and MSH2 were hub node degrees in down-regulated genes (Fig. 3B). As a result, MAPK11, AKT3, MYC, CCND1, CYCs, IGF1, TGFBR2, GRB10, SMAD4, TP53, and MSH2 were selected as hub genes for further analysis owing to the high degree of connectivity (Fig. 3C).

Figure 3 
figure 3

Protein–protein interaction (PPI) network construction. PPI network was constructed with the DEGs of GEO and TCGA datasets. (A,B) The significant module was identified from the PPI network using the FunRich software with a score of ≥ 7. Panel (A) shows the interaction between 193 down-regulated genes. Panel (B) shows the interaction between 100 up-regulated genes. Panel (C) shows the interaction between up-and down-regulated selected genes.

Construction of the lncRNA–miR–mRNA network

Figure 4 was created based on the lncRNA–miR–mRNA network that included 44 nodes and 153 edges. A total of 11 lncRNAs (Table 5), 12 miRs (Table 1), and 12 mRNAs (Table 4) was selected to construct the lncRNA–miR–mRNA network (Fig. 4).

Figure 4
figure 4

The lncRNA–miRNA–mRNA network. The network includes 44 nodes and 153 edges. The yellow, red, and blue ellipses represent the lncRNAs, miRs, and genes, respectively.

Experimental sampling

The demographic characteristics of the participants have been summarized in Table 8. During the weeks of follow-up, 5 patients, consisting of 3 patients in the probiotic group and 2 patients in the placebo group, withdrew from the study (Fig. 5). Finally, 107 patients diagnosed with rectum cancer (probiotic group: 53, placebo group: 54) finished the examinations. The mean age was 57.3 ± 11.5 years, and the majority of the participants (59.7%) were male. Regarding the disease's staging, the diagnoses were made at stage III (70.9%) and stage II (29.1%).

Table 8 Baseline characteristics of the participants in the present study.
Figure 5
figure 5

A flowchart of the present trial strategy.

The expression of onco- and tumor suppressor lncRNAs in pre-and post-intervention

The expression levels of the onco-lncRNAs, including CCAT1, LOC152578, UCA1, CRNDE, PVT1, MALAT1, XLOC_000303, XLOC_006844, BCAR4, and HOTAIR, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were significantly decreased following the probiotic consumption (Fig. 6A–J) (P < 0.05). Unlike CCAT1, LOC152578, and XLOC_006844, the expression levels of the other onco-lncRNAs did not exhibit significant changes after the placebo consumption. Nevertheless, the expression levels of the onco-lncRNAs were meaningfully lower in the probiotic users than the placebo group (P < 0.05).

Figure 6 
figure 6

The relative expression of the selected lncRNAs in the rectal cancer patients. The relative expression levels of the lncRNAs were normalized by using a reference RNA. The oncogenic lncRNAs included: (A) PVT1, (B) HOTAIR, (C) MALAT1, (D) UCA1, (E) CCAT1, (F) CRNDE, (G) XLOC_006844, (H) LOC152578, (I) XLOC-000303, and (J) BCAR4. Tumor suppressor lncRNAs included: (K) LincRNA-P21.

The expression level of tumor suppressor lncRNA, including LincRNA-P21, was significantly decreased in rectal cancer patients compared to the control group. It was dramatically increased following the probiotic consumption and was considerably higher in the probiotic users compared to the placebo group (Fig. 6K) (P < 0.05).

The expression of selected onco-and tumor suppressor miRs in pre-and post-intervention

The expression levels of oncomiRs, including miR-21, miR-20a, miR-20b, miR-424, miR-1244, miR-135b, and miR-224, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were considerably decreased following the probiotic consumption (P < 0.05) (Fig. 7A–G). Unlike miR-1244, miR-378a, and miR-224, the other oncomiRs exhibited no significant changes after the placebo consumption. Notably, the expression levels of the oncomiRs were meaningfully lower in the probiotic group than the placebo (P < 0.05).

Figure 7 
figure 7

The relative expression of the selected miRs in the rectal cancer patients. The relative expression levels of the miRs were normalized by using a reference RNA. The oncomiRs included: (A) miR-21, (B) miR-20a, (C) miR-20b, (D) miR-424, (E) miR-1244, (F) miR-135b, and (G) miR-224. Tumor suppressor miRs included: (H) miR-378a, (I) miR-548ac, (J) miR-34a, (K) miR-133b, and (L) miR-601.

Our results showed that the expression levels of all selected tumor suppressor miRs, including miR-548ac, miR-378a, miR-34a, miR-601, and miR-133b, were significantly decreased in the rectal cancer patients compared to the control group, which were considerably increased following the probiotic consumption (P < 0.05) (Fig. 7H–L). Except for miR-378a and miR-34a, the levels of the other tumor suppressor miRs exhibited no significant changes after the placebo consumption. Notwithstanding, the expression levels of the tumor-suppressor miRs were meaningfully higher in the probiotic than the placebo users (P < 0.05).

The expression of selected onco- and tumor suppressor genes in pre-and post-intervention

The expression levels of oncogenes, including SMAD4, IGF1, GRB10, BCL2, CCND1, MYC, AKT3, TGFBR2, and CYCS, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were considerably decreased following the probiotic consumption (P < 0.05) (Fig. 8A–I). Except for BCL2, SMAD4, MYC, and TGFBR2, the other oncogenes revealed no significant changes after the placebo consumption (Fig. 8A–I). Nonetheless, the expression levels of the oncogenes were significantly lower in the probiotic group than the placebo (P < 0.05).

Moreover, our results showed that the tumor suppressor genes' expression levels, including MAPK11, TP53, and MSH2, were significantly decreased in the rectal cancer patients compared to the control group, which were considerably increased following the probiotic consumption (P < 0.05) (Fig. 8J–L). The placebo consumption did not significantly impact the selected tumor suppressor genes (Fig. 8J–L). Interestingly, the expression levels of the selected tumor suppressor genes were meaningfully higher in the probiotic group than the placebo group (P < 0.05).

Figure 8 
figure 8

The relative expression of the candidate genes in rectal cancer patients. The relative expression level of the genes was normalized by using a reference gene. The oncogenes included: (A) AKT3, (B) MYC, (C) BCL2, (D) GRB10, (E) IGF1, (F) SMAD4, (G) CCND1, (H) CYCS, and (I) TGFBR2. Tumor suppressor genes included: (J) MAPK11, (K) MSH2, and (L) TP53.

Correlation of the lncRNA–miR–mRNA network

To further understand the role of differential expression of ceRNAs in rectal cancer, we performed a correlation analysis between lncRNAs, miRs, and mRNAs. Consequently, 11 lncRNAs, 12 miRs, and 12 mRNAs constituted a direct regulatory relationship of the lncRNA–miRNA–mRNA network (Fig. 9). Accordingly, there was a strong correlation between some network components, including miR-133b and IGF1 gene, miR-548ac and MSH2 gene, and miR-21 and SMAD4 gene. Likewise, we created a heat map of the expression of the selected lncRNAs, miRs, and mRNAs using CIMminer (https://discover.nci.nih.gov/cimminer/home.do) (Fig. 10).

Figure 9
figure 9

ceRNA regulatory network of lncRNAs, miRs, and mRNAs in rectal cancer. Red lines indicate a negative correlation, and green lines indicate a positive correlation. The figure was created using the R software: R Core Team (2019), R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. PVT: PVT1, HOT: HOTAIR, MAL: MALAT1, UCA: UCA1, CCT: CCAT1, CRE: CRNDE, XLOC_006: XLOC_006844, LOC: LOC152578, XLOC-000: XLOC-000303, BCA: BCAR4, LIN: LincRNA-P21, MAP: MAPK11, AKT: AKT3, MYC: MYC, BCL: BCL2, GRB: GRB10, IGF: IGF1, SMA: SMAD4, CCN: CCND1, CYC: CYCS, TGF: TGFBR2, MSH: MSH2,TP5: TP53, mR-21: miR-21, mR-20: miR-20a, mR-20b: miR-20b, mR-4: miR-424, mR-12: miR-1244, mR-135: miR-135b, mR-22: miR-224, mR-37: miR-378a, mR-5: miR-548ac, mR-34: miR-34a, mR-133: miR-133b, mR-6: miR-601.

Figure 10 
figure 10

A plot heatmap to show the gene expression profile of DEGs in both bioinformatics (A) and experiment data (− ΔCT) (B,C).

Discussion

This study investigated the effects of L. acidophilus consumption on the expression of lncRNAs, miRs, and mRNAs in patients with non-metastatic rectal cancer. Based on a biphasic methodology, we constructed a network of the lncRNA–miR–mRNA using bioinformatics analyses. 11 lncRNAs, 12 miRs, and 12 genes have displayed significant differential expressions in cancerous tissues compared to noncancerous tissues. Besides, our experimental results have shown that the L. acidophilus consumption was associated with an expressional improvement of candidate lncRNAs, miRs, and mRNAs compared to the placebo group.

Some genes can play a role in tumorigenesis and the progression of colorectal cancer. Accordingly, TGF-βR2 is a trans-membrane serine-threonine kinase and is the only known receptor complex for TGF-β to be phosphorylated. It, in turn, may phosphorylate downstream proteins, including the SMAD, PI3K, p38MAPK, PKA, and RhoA, leading to inhibiting cell proliferation, inducing apoptosis, terminating differentiation, and maintaining genetic stability. Furthermore, CCND1 expression was significantly related to lymph nodes and distant metastases. There was a significant statistical correlation between the CCND1 gene and high stages in colorectal cancer15. Here, we observed that the probiotic consumers had a lower expression level of CCND1 than the placebo group. Similar to our results, tumor suppressor genes such as the MAPK can also be up-regulated in probiotic consumption16. Our analysis showed a considerable interaction between the candidate DEGs and miRs such as miR-21, miR-20a, and miR-34a. The miR-21 overexpression is associated with a non-complete response to preoperative chemo-radiotherapy in patients with rectal adenocarcinomas17,18. Moreover, it was reported that c-Myc up-regulates the miR-17 and down-regulates the angiogenesis inhibitors. Dews et al. showed that the overexpression of miR-20a is associated with reduced TGF-βR2 protein levels in colon cells. They represented that the TGF-βR2 can be a direct target of miR-17/20a. This inhibition would deactivate the downstream mediators such as SMAD and thrombospondin type I, which can be associated with inhibition of angiogenesis in tumor cells19. In this setting, the miR-34 family is a transcriptional target of the p53, directly suppressing a set of canonical Wnt genes and Snail, resulting in p53-mediated suppression of Wnt signaling and the EMT process. Kim et al. reported that p53 could regulate GSK-3β nuclear localization via miR-34-mediated suppression of Axin2 in CRC20.

Although there is a strong link between changes in the intestinal microbiome and rectal cancer, the potential mediators of these relationships are unclear. Accordingly, our bioinformatics study analyzed the lncRNA–miR–mRNA network of the essence in rectal carcinogenesis. In this setting, several lncRNAs can target one miR by inhibiting its expression through various mechanisms. According to our results, HOTAIR, as a lncRNA, can negatively regulate the expression of miR-203a-3p, miR-545, and miR-218, leading to EGFR and VOPP1 regulation, which can be found to be related to chemotherapy resistance in rectal cancer21. LncRNAs can bind to miR-34a and disrupt the regulation of miRs and target genes, including GAPLINC and SNHG7, which may increase, migrate, and invade rectal cancer cells22,23. While lncRNAs have the necessary pathological properties for appropriate biomarkers, their extraction and measurement are limited. As a result, although several studies reported differences in the expression of new lncRNA markers in human plasma and serum, others have difficulty replicating21. However, there is no doubt that lncRNAs and miRs are essential players in cancer pathology and can be a significant regulator of CRC's biology in cell cultures, animal models, and human samples. Future systematic and integrated analysis of different RNA molecules with potential cross-discussion may greatly help unravel the complex mechanisms of tumorgenesis and treatment of rectal cancer.

The experiments and trials regarding the beneficial effects of probiotics in the cancer region showed promising results. Commensal Lactobacillus species (such as L. acidophilus) are normal inhabitants of the natural microbiota24. Importantly, probiotics have been shown to reduce colon cancer incidence in animal models10,25. Oral administration of L. acidophilus has effectively reduced colon carcinoma growth, suggesting that its consumption was associated with suppressed tumor growth10,26. In an animal model, Chen et al. reported that L. acidophilus could induce apoptosis of colon cancer cells by down-regulating BCL-2 expression and up-regulating caspase-3 and -927. Urbanska et al. explained that oral administration of L. acidophilus in a yogurt formulation in Apc (Min/þ) mice minimized intestinal inflammation and delayed overall polyp progression. They showed that pre-inoculation with L. acidophilus in Bulb/c mice resulted in retarding tumor volume growth, lowered histopathology scores, enhanced apoptosis of tumor cells, and down-regulated surface proteins' expression25. Besides, Yue et al. found that the metabolites of L. acidophilus could suppress the cell metastasis of colon cancer by inhibiting the VEGF/MMPs signaling pathway28. Agah et al. showed that L. acidophilus could induce a lower level of CEA and CA19-9 and a higher level of IFN-γ in the azoxymethane-induced colon cancer by altering the T-Cell signature to increase CD4+ and CD8+ cells10,29. Moreover, Caramés et al. revealed the anti-cancer effects of L. acidophilus by expressing antioxidant enzymes on an animal colon cancer17.

Accordingly, probiotics could alleviate the complication of CRC patients who underwent surgery or chemoradiation therapy30. Kim et al. found that radiation causes significant changes in the microbiome abundance and diversity31 that can influence the effectiveness of the anti-cancer treatments. Moreover, the immune microenvironment may modulate radiosensitivity related to radiation injury. Current evidence supports the use of probiotics as adjunctive therapy. They might have beneficial effects on some aspects of toxicity related to radiotherapy. It seems that probiotics could be safely administered even in neutropenia32. A meta-analysis study showed that probiotics could reduce the incidence of diarrhea induced by radiotherapy and have beneficial effects in preventing radiation-induced diarrhea, especially for grade ≥ 2 or 3 diarrhea. They may be a safe, promising therapeutic alternative for cancer patients suffering radiotherapy-induced diarrhea33. In addition, probiotics have been shown to reduce tumor recurrence rates and protect the intestinal mucosa's physical and biological barrier functions. They can improve the integrity of the intestinal epithelial layer and increase resistance to pathogenic colonization34. They can also produce a fasting-induced adipose factor, a gut radioprotector35. Moreover, two probiotic strains, including Lactobacillus fermentum and Lactobacillus salivarius, could re-establish miR-155 and miR-223 expression, preserve the mucosal barrier function, and relieve the DSS-induced colitis36. Tan et al. performed a comprehensive analysis of the lncRNA–miR–mRNA regulatory network for microbiota-mediated colorectal cancer37. They showed that probiotics could regulate lncRNAs' expression levels by competitively binding to the corresponding miRs and mRNAs, called ceRNA regulatory network38. These researchers identified a set of microbiota-mediated biomarkers and constructed ceRNA networks in CRC. Accordingly, 75 DELs, 8 DEMs, and 9 DEGs in the probiotic-related ceRNA network were obtained. They exhibited that the probiotics could inhibit the oncogenes' expression, including miR-153 and miR-429, and promote the tumor suppressors' expression, including miR-140 and miR-13237. They also showed that four lncRNAs from the microbiota-mediated ceRNA network, including LINC00355, KCNQ1OT1, LINC00491, and HOTAIR, were found to be associated with poor overall survival. These results could indicate a potential mechanism where probiotics can regulate immune system functions in CRC.

Here, we have demonstrated that the administration of probiotics could improve the molecular profile of rectal cancer patients. This novel effect yielded that probiotics could play more fundamental roles in CRC management and co-administration with chemoradiation therapies to reduce complications and increase their efficacy. Likewise, similar outcomes were pursued by an unpublished study (NCT03072641) aiming to determine if probiotics could alleviate the cancer-associated gut microbiota and epigenetic alterations in CRC. Moreover, Zaharudinn et al. reported that the probiotics containing six viable microorganisms could reduce the post-surgical pro-inflammatory cytokines such as TNF-α, IL-6, IL-10, and IL-12 in CRC patients39. However, comprehensive research should be assumed better to understand the clinical values of probiotics in colorectal cancer. Therefore, it will be with much more clinical efficacy if the clinicians and researchers apply mechanism-oriented and population-specific approaches when dealing with probiotics.

Conclusion

During radiotherapy, L. acidophilus consumption in rectal cancer patients for 13 weeks could reduce oncogenic lncRNAs, miRs, and mRNAs and simultaneously increase tumor-suppressor lncRNAs, miRs, and mRNAs. Our results suggest interactions among lncRNAs, miRs, and genes may mediate host-microbial interactions in rectal cancer and can be an explicit goal for developing treatment strategies. Moreover, promising therapeutic approaches for activating endogenous miR expression to mediate lncRNA silencing mediated by target miRs have been proposed, although more works need to be evaluated.

Materials and methods

Study setting

This study is part of an ongoing randomized clinical trial registered in the Iranian randomized control trial (NO: IRCT2014092118745N3). The study is a randomized, double-blind, and single-center conducted on 107 new cases (55 males and 52 females) with non-metastatic rectal cancer at Emam-Khomeini Hospital, Tehran, Iran, which entered into the study on 08-11-2014 (Fig. 5). All participants were informed of the current research objectives, study protocol, and informed consent to participate in the study.

Study population

After reviewing the medical records of patients who had previously been confirmed diagnosing rectal cancer based on pathologic reports, the eligible cases were recruited. Inclusion criteria comprised age between 30 and 70 years, non-metastatic stage II or III rectal cancer, Karofsky Performance Status ≥ 70 or Eastern Cooperative Oncology Group = 0–1, no history of familial colorectal cancer, and no history of probiotic or symbiotic consumption in the last three months. Exclusion criteria included the history of other cancers, intestinal obstructions, viral hepatitis or HIV history, and patients with severe neutropenia.

Randomization, allocation, and interventions

Randomization was performed on block randomization. The block randomization was performed based on blocks of 2, and the computer program performed it. Sealed envelopes with the treatment codes were stored in the same department. The patients were blinded using an identical capsule to those given to the intervention group as a placebo. Besides, the caregiver and the laboratory staff were all blinded to the patient’s medical documents. Similarly, the statistician who performed the statistical analyses was also blinded to the grouping codes assigned in the dataset. The patients in the probiotic group received probiotic capsules (500 mg) (109 CFU) for 13 weeks, taking the tablets three times a day. The subjects in the placebo group received placebo capsules with the same shape, color, and smell as the probiotic group's protocol.

Primary outcomes

We measured and compared the expression levels of candidate lncRNAs (Table 5), miRs (Table 1), and mRNAs (Table 4) before (baseline) and after three months of the intervention in the probiotic and placebo groups.

Identification of differentially expressed genes, miRs, and lncRNAs in rectal cancer datasets

The platforms used for miRs and mRNAs, including the miRs' expression profile (GSE128446 and GSE156732) and the mRNAs' expression profile (GSE68204: GPL6480, GSE110224, and GSE113513), were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). GSEs' data were downloaded for use with the GEOquery R package (https://bioconductor.org/packages/GEOquery)40. We analyzed the candidate miRs, lncRNAs, and genes with P-value < 0.05 and |LogFC|> 1 in the dataset as DEGs, differentially expressed miRNAs (DEMs), and differentially expressed lncRNAs (DELs). Moreover, the GEPIA2 (http://gepia2.cancer-pku.cn), the cBioPortal (https://www.cbioportal.org), and the Broad Institute’s FireBrowse (http://firebrowse.org) are websites for analyzing the differential expression genes from the TCGA and Genotype-Tissue Expression projects41. The used platforms for miRs included the OncomiR (http://www.oncomir.umn.edu/omcd/), miRGator 3.0 (https://tools4mirs.org), and miRCancerdb (http://mircancer.ecu.edu) databases of TCGA dataset42. The used databases for lncRNAs included LncRNADisease (http://www.rnanut.net/lncrnadisease) and Lnc2Cancer v3.0 (http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) of TCGA dataset43,44. Figure 11 shows a flowchart diagram for used bioinformatics analysis.

Figure 11
figure 11

A flowchart diagram for used bioinformatics analysis in the present study.

Predicted target genes and lncRNAs of candidate miRs

The target genes of miRs were identified using online predictive programs such as miRmap (https://mirmap.ezlab.org/app/), miRWalk2 (http://zmf.umm.uni-heidelberg.de/apps/zmf /mirwalk2/), TargetScan Release 7.0 (http://www.targetscan.org), and miRTarBase (https://mirtarbase.cuhk.edu.cn/miRTarBase) datasets. Furthermore, the lncRNAs that regulate DEMs were collected by the LncRNA2target, TANRIC, and lncBase (https://diana.e-ce.uth.gr/lncbasev3) datasets. The lncRNA–miR–mRNA network was constructed by the selected miRs, lncRNAs, and mRNAs. The results were visualized by Cytoscape_v3.1 (https://cytoscape.org) software45.

Construction and analysis of the lncRNA–miR–mRNA network

The lncRNA–miR–mRNA network was constructed and visualized using Cytoscape software based on the ceRNA theory. Here, the nodes and edges were used to represent extensive biological data. Intuitively, each node represents a biological molecule, and the edges stand for the interactions between nodes and the node degrees. The edges were calculated to exploit the hub nodes that possess essential biological functions46. A network analysis was performed using Cytoscape software to explore the structure and feature of the lncRNA–miR–mRNA competing triplets. Topological parameters of standard centrality measures in a network, including DC, BC, and CC, were assessed. The DC is defined as the number of links incident upon a node. The BC for each node is calculated as the number of these shortest paths that pass through the node. The CC is the length of the shortest paths between the node and other nodes in the network.

Correlation of lncRNA–miR–mRNA network

We constructed a heat map based on our experimental data to show the possible correlation of the selected lncRNAs, miRs, and mRNAs. The absolute value of the correlation coefficient (equal to or more than 0.5) represented a significant correlation. We matched interactions of miRs according to the miR-code database (http://www.mircode.org/) by the differentially expressed lncRNA and miRs. Moreover, the target genes of miRs were created using the mentioned databases. At last, a ceRNA network between lncRNAs, miRs, and mRNAs was constructed using Cytoscape 3.1 software.

The analysis of the GO term and KEGG pathways by FunRich software

The FunRich (http://www.funrich.org) is software for functional gene classification. The GO and KEGG (map05210)47 enrichment analyses of the DEGs were executed through the FunRich software.

Sample collection

Before and after the intervention, 10 ml blood samples were obtained from all the subjects using disposable vacutainer blood collection tubes. The blood was centrifuged at 3000g for 5 min, and peripheral blood mononuclear cells (PBMCs) were then isolated by the Ficoll-Hypaque (Amersham). The cells were suspended into 90% Foetal Bovin Serum (FBS) (life tech)/10% Dimethyl sulfoxide (DMSO) (Sigma), and the plasma and PBMCs were then preserved at − 80 °C.

Real-time PCR analysis

According to the manufacturer's instructions, the total RNA was extracted from the plasma and PBMC samples. Plasma (250 μl) and PBMC (500 μl) were added to 750 μl and 500 μl TRIzol (Beijing Tiangen Biotech Co., Ltd.), respectively. The absorbance ratio (A260/280) of total RNA, between 1.8 and 2.2, was determined using an ultraviolet (UV) spectrophotometer. According to the manufacturer's instructions, the miRcute miRNA cDNA First-Strand Synthesis kit (Beijing Tiangen Biotech Co., Ltd) to quantify miRs and the cDNA Synthesis Kit (TAKARA BIO INC. Cat. 6 30 v.0708) to quantify genes and lncRNAs were used. Then, cDNA was used in each Real-Time PCR assay with the miRcute miRNA Fluorescence Quantitative Detection kit (Tiangen Biotech Co., Ltd.). The cycling conditions were pre-denaturation at 94 °C for 2 min, followed by 40 cycles of 94 °C for 20 s and 60 °C for 34 s. The SYBR Green method (AccuPower Green Star qPCR Master Mix; Bioneer, Korea) was used for genes and lncRNAs.

PCR cycling was performed for one cycle at 95 °C for 10 min, 40 cycles at 95 °C for 20 s, and 60 °C for 45 s. The melting curve analysis was run from 60 to 95 °C to confirm specific amplification48,49. The expression of U6 and B-actin was used to normalize miRs, lncRNAs, and genes as the Internal Reference Gene. The list of primers has shown in Table 9. The qRT-PCR reactions were performed using an ABI StepOne plus System (Applied Biosystems; Thermo Fisher Scientific, Inc). The expression level of the genes was calculated using the − ΔCT method. ΔCT was calculated by subtracting the CT values of U6 and B-actin from the CT values of the targets50. The expression data generated from our study samples have been available as a supplementary information file (Supplementary Information).

Table 9 The used primers for real-time PCR.

Statistical analysis

The statistical analysis was carried out using GraphPad Prism 6.01 (https://www.graphpad.com) software. The one-sample K–S test was used to evaluate the normality of the data. The t-test and ANOVA were used to analyze the data in two and multiple groups. The descriptive analysis for quantitative data was performed using mean ± SD. The same analysis was performed for qualitative data by representing the frequencies and regarded percentages. We constructed a correlation network of the selected lncRNA–miR–mRNA using the R Core Team (2019), R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org. The statistical significance was defined as P < 0.05.

Ethical approval

All methods were performed under the relevant guidelines and regulations. All procedures performed in studies involving human participants were under the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The experimental procedures and care protocols were approved by a review board committee of Tehran University of Medical Sciences and Alborz University of Medical Sciences (NO: IR.TUMS.IKHC.REC.1397.036 and IR.ABZUMS.REC.1398.154) and registered by the Iranian Randomized Control Trial (IRCT) ethical board (NO: IRCT2014092118745N3). Written informed consent was obtained from each participant before the sample collection.

Data availability

The data that support the findings of this study are available from the corresponding author, AMA, upon reasonable request.

References

  1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  Google Scholar 

  2. Wong, S. H. & Yu, J. Gut microbiota in colorectal cancer: Mechanisms of action and clinical applications. Nat. Rev. Gastroenterol. Hepatol. 16, 690–704. https://doi.org/10.1038/s41575-019-0209-8 (2019).

    CAS  Article  PubMed  Google Scholar 

  3. Wang, T. et al. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 6, 320–329. https://doi.org/10.1038/ismej.2011.109 (2012).

    CAS  Article  PubMed  Google Scholar 

  4. Dalmasso, G. et al. Microbiota modulate host gene expression via microRNAs. PLoS One 6, e19293. https://doi.org/10.1371/journal.pone.0019293 (2011).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Williams, M. R., Stedtfeld, R. D., Tiedje, J. M. & Hashsham, S. A. MicroRNAs-based inter-domain communication between the host and members of the gut microbiome. Front. Microbiol. 8, 1896. https://doi.org/10.3389/fmicb.2017.01896 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tarallo, S. et al. Altered fecal small RNA profiles in colorectal cancer reflect gut microbiome composition in stool samples. mSystems. https://doi.org/10.1128/mSystems.00289-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Liu, S. et al. The host shapes the gut microbiota via fecal microRNA. Cell Host Microbe 19, 32–43. https://doi.org/10.1016/j.chom.2015.12.005 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Drebber, U. et al. Altered levels of the onco-microRNA 21 and the tumor-supressor microRNAs 143 and 145 in advanced rectal cancer indicate successful neoadjuvant chemoradiotherapy. Int. J. Oncol. 39, 409–415 (2011).

    PubMed  Google Scholar 

  9. Yuan, C., Burns, M. B., Subramanian, S. & Blekhman, R. Interaction between host MicroRNAs and the gut microbiota in colorectal cancer. MSystems 3, e00205-17 (2018).

    Article  Google Scholar 

  10. Heydari, Z. et al. Effects of Lactobacillus acidophilus and Bifidobacterium bifidum probiotics on the expression of microRNAs 135b, 26b, 18a and 155, and their involving genes in mice colon cancer. Probiotics Antimicrob. Proteins 11, 1155–1162 (2019).

    CAS  Article  Google Scholar 

  11. Rodríguez-Nogales, A. et al. Intestinal anti-inflammatory effect of the probiotic Saccharomyces boulardii in DSS-induced colitis in mice: Impact on microRNAs expression and gut microbiota composition. J. Nutr. Biochem. 61, 129–139 (2018).

    Article  Google Scholar 

  12. Gianotti, L. et al. A randomized double-blind trial on perioperative administration of probiotics in colorectal cancer patients. World J. Gastroenterol. WJG 16, 167 (2010).

    CAS  Article  Google Scholar 

  13. Ma, Y., Zhang, J., Wen, L. & Lin, A. Membrane-lipid associated lncRNA: A new regulator in cancer signaling. Cancer Lett. 419, 27–29 (2018).

    CAS  Article  Google Scholar 

  14. Tay, Y., Rinn, J. & Pandolfi, P. P. The multilayered complexity of ceRNA crosstalk and competition. Nature 505, 344–352 (2014).

    ADS  CAS  Article  Google Scholar 

  15. Balcerczak, E. et al. Cyclin D1 protein and CCND1 gene expression in colorectal cancer. Eur. J. Surg. Oncol. 31, 721–726. https://doi.org/10.1016/j.ejso.2005.04.005 (2005).

    CAS  Article  PubMed  Google Scholar 

  16. Slattery, M. L., Lundgreen, A. & Wolff, R. K. MAP kinase genes and colon and rectal cancer. Carcinogenesis 33, 2398–2408. https://doi.org/10.1093/carcin/bgs305 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Carames, C. et al. MicroRNA-21 predicts response to preoperative chemoradiotherapy in locally advanced rectal cancer. Int. J. Colorectal Dis. 30, 899–906. https://doi.org/10.1007/s00384-015-2231-9 (2015).

    Article  PubMed  Google Scholar 

  18. Campayo, M. et al. miR-21, miR-99b and miR-375 combination as predictive response signature for preoperative chemoradiotherapy in rectal cancer. PLoS One 13, e0206542. https://doi.org/10.1371/journal.pone.0206542 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Dews, M. et al. The myc-miR-17~92 axis blunts TGF{beta} signaling and production of multiple TGF{beta}-dependent antiangiogenic factors. Cancer Res. 70, 8233–8246. https://doi.org/10.1158/0008-5472.CAN-10-2412 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Kim, N. H. et al. p53 regulates nuclear GSK-3 levels through miR-34-mediated Axin2 suppression in colorectal cancer cells. Cell Cycle 12, 1578–1587. https://doi.org/10.4161/cc.24739 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Cantile, M., Di Bonito, M., Tracey De Bellis, M. & Botti, G. Functional interaction among lncRNA HOTAIR and microRNAs in cancer and other human diseases. Cancers 13, 570 (2021).

    CAS  Article  Google Scholar 

  22. Li, Y. et al. Long non-coding RNA-SNHG7 acts as a target of miR-34a to increase GALNT7 level and regulate PI3K/Akt/mTOR pathway in colorectal cancer progression. J. Hematol. Oncol. 11, 1–17 (2018).

    Article  Google Scholar 

  23. Luo, Y. et al. Long noncoding RNA GAPLINC promotes cells migration and invasion in colorectal cancer cell by regulating miR-34a/c-MET signal pathway. Dig. Dis. Sci. 63, 890–899 (2018).

    CAS  Article  Google Scholar 

  24. Du, P., Kibbe, W. A. & Lin, S. M. lumi: A pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008).

    CAS  Article  Google Scholar 

  25. Urbanska, A. M., Bhathena, J., Martoni, C. & Prakash, S. Estimation of the potential antitumor activity of microencapsulated Lactobacillus acidophilus yogurt formulation in the attenuation of tumorigenesis in Apc(Min/+) mice. Dig. Dis. Sci. 54, 264–273. https://doi.org/10.1007/s10620-008-0363-2 (2009).

    Article  PubMed  Google Scholar 

  26. Ranji, P., Agah, S., Heydari, Z., Rahmati-Yamchi, M. & Alizadeh, A. M. Effects of Lactobacillus acidophilus and Bifidobacterium bifidum probiotics on the serum biochemical parameters, and the vitamin D and leptin receptor genes on mice colon cancer. Iran. J. Basic Med. Sci. 22, 631 (2019).

    PubMed  PubMed Central  Google Scholar 

  27. Chen, C. C. et al. Oral inoculation of probiotics Lactobacillus acidophilus NCFM suppresses tumour growth both in segmental orthotopic colon cancer and extra-intestinal tissue. Br. J. Nutr. 107, 1623–1634. https://doi.org/10.1017/S0007114511004934 (2012).

    CAS  Article  PubMed  Google Scholar 

  28. Yue, Y. C. et al. Metabolite secretions of Lactobacillus plantarum YYC-3 may inhibit colon cancer cell metastasis by suppressing the VEGF-MMP2/9 signaling pathway. Microb. Cell Fact. 19, 213. https://doi.org/10.1186/s12934-020-01466-2 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Agah, S. et al. More protection of Lactobacillus acidophilus than Bifidobacterium bifidum probiotics on azoxymethane-induced mouse colon cancer. Probiotics Antimicrob. Proteins 11, 857–864 (2019).

    CAS  Article  Google Scholar 

  30. Mego, M. et al. Prevention of irinotecan induced diarrhea by probiotics: A randomized double blind, placebo controlled pilot study. Complement. Ther. Med. 23, 356–362. https://doi.org/10.1016/j.ctim.2015.03.008 (2015).

    Article  PubMed  Google Scholar 

  31. Kim, Y. S., Kim, J. & Park, S.-J. High-throughput 16S rRNA gene sequencing reveals alterations of mouse intestinal microbiota after radiotherapy. Anaerobe 33, 1–7 (2015).

    CAS  Article  Google Scholar 

  32. Mego, M. et al. Probiotic bacteria in cancer patients undergoing chemotherapy and radiation therapy. Complement. Ther. Med. 21, 712–723 (2013).

    Article  Google Scholar 

  33. Qiu, G., Yu, Y., Wang, Y. & Wang, X. The significance of probiotics in preventing radiotherapy-induced diarrhea in patients with cervical cancer: A systematic review and meta-analysis. Int. J. Surg. 65, 61–69 (2019).

    Article  Google Scholar 

  34. Ho, C. L. et al. Author Correction: Engineered commensal microbes for diet-mediated colorectal-cancer chemoprevention. Nat. Biomed. Eng. 4, 754–755 (2020).

    Article  Google Scholar 

  35. Crawford, P. A. & Gordon, J. I. Microbial regulation of intestinal radiosensitivity. Proc. Natl. Acad. Sci. 102, 13254–13259 (2005).

    ADS  CAS  Article  Google Scholar 

  36. Rodríguez-Nogales, A. et al. Differential intestinal anti-inflammatory effects of Lactobacillus fermentum and Lactobacillus salivarius in DSS mouse colitis: Impact on microRNAs expression and microbiota composition. Mol. Nutr. Food Res. 61, 1700144 (2017).

    Article  Google Scholar 

  37. Tan, X. et al. Comprehensive analysis of lncRNA–miRNA–mRNA regulatory networks for microbiota-mediated colorectal cancer associated with immune cell infiltration. Bioengineered 12, 3410–3425 (2021).

    CAS  Article  Google Scholar 

  38. Zou, D. et al. Bioinformatics analysis reveals the competing endogenous RNA (ceRNA) coexpression network in the tumor microenvironment and prognostic biomarkers in soft tissue sarcomas. Bioengineered 12, 662–672 (2021).

    Article  Google Scholar 

  39. Zaharuddin, L., Mokhtar, N. M., Muhammad Nawawi, K. N. & Raja Ali, R. A. A randomized double-blind placebo-controlled trial of probiotics in post-surgical colorectal cancer. BMC Gastroenterol. 19, 131. https://doi.org/10.1186/s12876-019-1047-4 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Wang, Z. et al. Identification of potential diagnostic and prognostic biomarkers for colorectal cancer based on GEO and TCGA databases. Front. Genet. 14, 602922 (2021).

    Article  Google Scholar 

  41. Yu, D.-C. et al. Transcript levels of spindle and kinetochore-associated complex 1/3 as prognostic biomarkers correlated with immune infiltrates in hepatocellular carcinoma. Sci. Rep. 11, 1–12 (2021).

    ADS  Article  Google Scholar 

  42. Sarver, A. L., Sarver, A. E., Yuan, C. & Subramanian, S. O. M. C. D. OncomiR cancer database. BMC Cancer 18, 1–6 (2018).

    Article  Google Scholar 

  43. Gao, Y. et al. Lnc2Cancer 3.0: An updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res. 49, D1251–D1258 (2021).

    CAS  Article  Google Scholar 

  44. Bao, Z. et al. LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res. 47, D1034–D1037 (2019).

    CAS  Article  Google Scholar 

  45. Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  Article  Google Scholar 

  46. Kohl, M., Wiese, S. & Warscheid, B. Data mining in proteomics: From standards to applications. Springer Protoc. 696, 1064–3745 (2011).

    Google Scholar 

  47. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  Article  Google Scholar 

  48. Shen, Y. et al. Prognostic impact of mutation profiling in patients with stage II and III colon cancer. Sci. Rep. 6, 1–7 (2016).

    CAS  Article  Google Scholar 

  49. Flavin, R. J. et al. Potentially important microRNA cluster on chromosome 17p13.1 in primary peritoneal carcinoma. Mod. Pathol. 22, 197–205 (2009).

    CAS  Article  Google Scholar 

  50. Khalighfard, S. et al. A systematic approach introduced novel targets in rectal cancer by considering miRNA/mRNA interactions in response to radiotherapy. Cancer Biomark. 33, 97–110 (2022).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

Research reported in this publication was supported by Elite Researcher Grant Committee under award number [Grant Number: 971158] from the National Institute for Medical Research Development (NIMAD), Tehran, Iran. This study was also co-funded by Tehran University of Medical Sciences (Grant Number: 34918) and Alborz University of Medical Sciences (Grant Number: 3107).

Funding

None of the funding sources had any role in the study design, the collection, analysis, and interpretation of data, or the decision to submit the article for publication.

Author information

Authors and Affiliations

Authors

Contributions

Z.K. and M.M.N.: study conception and manuscript preparation. S.K.: sample collection, sample processing, data analysis, and manuscript preparation. M.G.Z., M.G., and V.K.: sample processing and data analysis. T.A. and M.R.: manuscript preparation and data analysis. A.M.A.: study conception and design and manuscript revision.

Corresponding author

Correspondence to Ali Mohammad Alizadeh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Khodaii, Z., Mehrabani Natanzi, M., Khalighfard, S. et al. Novel targets in rectal cancer by considering lncRNA–miRNA–mRNA network in response to Lactobacillus acidophilus consumption: a randomized clinical trial. Sci Rep 12, 9168 (2022). https://doi.org/10.1038/s41598-022-13297-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-022-13297-9

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing