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Abstract Background Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics. Objective To develop gut bacterial subtypes and explore potential microbial targets for CRC. Methods Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC‐associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA). Results Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia‐Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes. Conclusion Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC.

Many studies have reported the toxic effects of microplastics (MPs) on organisms, especially on how conventional plastics affect organisms after short-term exposure. The effects of biodegradable plastics on organisms are, however, largely unexplored, especially concerning their impact after long-term exposure. We perform a series of experiments to examine the effects of conventional (polyethylene (PE)) and biodegradable (polylactic acid (PLA)) microplastics on earthworms at three concentrations (0.5 %, 2 %, and 5 % (w/w)) and particle sizes (149, 28, and 13 μm) over short- (14 d) and long-term (28 d) periods of exposure. Negative effects on earthworms are more pronounced following exposure to PE than PLA, particularly over the shorter term. After longer-term exposure, earthworms may adapt to PE and PLA environments. A close relationship exists between the effects of MPs on earthworms and activities of superoxide dismutase, catalase, and malondialdehyde enzymes, which we use to evaluate the degree of antioxidant damage. We report both PE and PLA to negatively affect earthworms, but for the effects of PLA to be less severe after longer-term exposure. Further investigation is required to more fully assess the potential negative effects of PLA use on soil organisms in agriculture.

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Abstract Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti‐cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real‐time monitoring in both pre‐treatment assessment and post‐treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour‐associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high‐sensitivity and high‐resolution imaging. Acoustic imaging is a real‐time and non‐invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation‐free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in‐depth study on tumour‐associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

Abstract Background Emergence of novel immuno‐therapeutics has shown promising improvement in the clinical outcome of colorectal cancer (CRC). Objective To identify robust immune checkpoints based on expression and immune infiltration profiles of clinical CRC samples. Methods One dataset from The Cancer Genome Atlas database and two from Gene Expression Omnibus were independently employed for the analysis. Genes associated with overall survival were identified, and distribution of each immune checkpoint with respect to different clinical features was determined to explore key immune checkpoints. Multiple staining methods were used to verify the correlation between key immune checkpoint ICOS and clinical pathological features. Differentially expressed mRNA and long non‐coding RNA (lncRNA) were then detected for gene set enrichment analysis and gene set variation analysis to investigate the differentially enriched biological processes between low‐ and high‐expression groups. Significant immune‐related mRNAs and lncRNA were subjected to competing endogenous RNA (ceRNA) network analysis. Correlation of inducible T‐cell costimulator (ICOS) and top 10 genes in ceRNA network were further considered for validation. Results ICOS was identified from 14 immune checkpoints as the most highly correlated gene with survival and clinical features in CRC. The expression of ICOS protein in the poorly differentiated group was lower than that in the moderately differentiated group, and the expression in different pathological stages was significant. In addition, the expressions of ICOS were negatively correlated with Ki67. A conspicuous number of immune‐related pathways were enriched in differentially expressed genes in the ICOS high‐ and low‐expression groups. Integration with immune infiltration data revealed a multitude of differentially expressed immune‐related genes enriched for ceRNA network. Furthermore, expression of top 10 genes investigated from ceRNA network showed high correlation with ICOS. Conclusion ICOS might serve as a robust immune checkpoint for prognosis with several genes being potential targets of ICOS‐directed immunotherapy in CRC.

Abstract Background Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). Methods A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. Results The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Conclusion Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.

Abstract Introduction Pancreatic follicular dendritic cell sarcoma (FDCS) is an exceptionally rare and low-to-moderate malignancy, with only seven reported cases to date. Clinical diagnosis of FDCS is challenging due to the lack of distinct biological and radiographic features. Case presentation A 67-year-old woman presented to the hospital with a 4-day history of severe abdominal pain. Imaging studies (CT and MRI) revealed a large cystic mass located at the tail of the pancreas, which was suspected to be myeloid sarcoma (MS) based on EUS and CT-guided pancreatic puncture. Postoperative pathology and immunohistochemistry confirmed the diagnosis of pancreatic FDCS. After the diagnosis was confirmed, the patient received postoperative chemotherapy with the CHOP regimen. At 11 months of follow-up, there was no evidence of recurrence. Seven published cases have been reviewed to comprehensively summarize the clinical characteristics, diagnosis, and treatment options of FDCS. Conclusion While imaging can be useful in detecting pancreatic FDCS, it should be interpreted with caution as it can be challenging to differentiate from other pancreatic tumors. Pathology and immunohistochemistry are considered the gold standard for diagnosis, with CD21, CD23, and CD35 being specific tumor cell markers. However, preoperative diagnosis of pancreatic FDCS remains difficult, and the pancreatic puncture may further increase the risk of misdiagnosis. The disease is highly prone to recurrence and metastasis, and surgery is the preferred method for both diagnosis and treatment of localized disease.

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.

Abstract Background The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer. Objective To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice. Methods Gastric juice samples were collected from 61 healthy people and 78 patients with gastric cancer (48 cases of early gastric cancer and 30 cases of advanced gastric cancer). The bacterial 16 S rRNA V1-V4 region of gastric juice samples was sequenced. The Shannon index, Simpson index, Ace index and Chao index were used to analyze the diversity of gastric juice samples. The RDP classifier Bayesian algorithm was used to analyze the community structure of 97% OTU representative sequences with similar levels. Linear discriminant analysis and ST-test were used to analyze the differences. Six machine learning algorithms, including the logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Catboost algorithm and gradient lifting tree algorithm, were used to construct risk prediction models for gastric cancer and advanced gastric cancer. Results The microbiota diversity and the abundance of bacteria was different in the healthy group, early gastric cancer and advanced gastric cancer (P

Cell mediated immune escape, a microenvironment factor, induces tumorigenesis and metastasis. The purpose of this study was to display the characteristics of T cell populations in immune microenvironments for colorectal cancer (CRC) metastasis. Unsupervised cluster analysis was conducted to identify functionally distinct T cell clusters from 3,003 cells in peripheral blood and 4,656 cells in tissues. Subsequently, a total of 8 and 4 distinct T cell population clusters were identified from tumor tissue and peripheral blood, respectively. High levels of CD8+TEX, CD4+TRM, TH1-like T cells, CD8+TEM, tumor-Treg from tissues, and CD4+TN from peripheral blood are essential components of immune microenvironment for the prediction of CRC metastasis. Moreover, exhausted T cells are characterized by higher expression of multiple inhibitory receptors, including PDCD1 and LAG3. Some genes such as PFKFB3, GNLY, circDCUN1D4, TXNIP and NR4A2 in T cells of cluster were statistically different between CRC metastasis and non-metastasis. The ligand-receptor interactions identified between different cluster cells and metastases-related DEGs identified from each cluster revealed that the communications of cells, alterations of functions, and numbers of T subsets may contribute to the metastasis of CRC. The mutation frequency of KiAA1551, ATP8B4 and LNPEP in T cells from tissues and SOR1 from peripheral blood were higher in metastatic CRC than that in non-metastatic CRC. In conclusion, the discovery of differential genes in T cells may provide potential targets for immunotherapy of CRC metastasis and relevant insights into the clinical prediction and prognosis of CRC metastasis.

Escherichia coli (E. coli) is a ubiquitous symbiotic bacterium in the gut, and the diversity of E. coli genes determines the diversity of its functions. In this review, the two‐edged sword theory was innovatively proposed. For the question 'how can we harness the ambivalent nature of E. coli to screen and treat CRC?', in terms of CRC screening, the variations in the abundance and subtypes of E. coli across different populations present an opportunity to utilise it as a biomarker, while in terms of CRC treatment, the natural beneficial effect of E. coli on CRC may be limited, and engineered E. coli, particularly certain subtypes with probiotic potential, can indeed play a significant role in CRC treatment. It seems that the favourable role of E. coli as a genetic tool lies not in its direct impact on CRC but its potential as a research platform that can be integrated with various technologies such as nanoparticles, imaging methods, and synthetic biology modification. The relationship between gut microflora and CRC remains unclear due to the complex diversity and interaction of gut microflora. Therefore, the application of E. coli should be based on the 'One Health' view and take the interactions between E. coli and other microorganisms, host, and environmental factors, as well as its own changes into account. In this paper, the two‐edged sword role of E. coli in CRC is emphasised to realise the great potential of E. coli in CRC screening and treatment. [ABSTRACT FROM AUTHOR]

ABSTRACT The occurrence and development of colorectal cancer (CRC) and advanced adenoma (AA) are closely related to the gut microbiome, and AA has a high cancerization progression rate to CRC. Current studies have revealed that bacteriological analysis cannot identify CRC from AA. The objective was to explore microbial targets that could identify CRC and AA from a microecological perspective and to figure out the best way to identify CRC based on fecal microbes. The metagenomic sequencing data were used to describe the gut microbiome profile and analyze the differences between microbial abundance and microbial single nucleotide polymorphism (SNP) characteristics in AA and CRC patients. It was found that there were no significant differences in the diversity between the two groups. The abundance of bacteria (e.g., Firmicutes, Clostridia, and Blautia), fungi (Hypocreales), archaea (Methanosarcina, Methanoculleus, and Methanolacinia), and viruses (Alphacoronavirus, Sinsheimervirus, and Gammaretrovirus) differed between AA and CRC patients. Multiple machine-learning algorithms were used to establish prediction models, aiming to identify CRC and AA. The accuracy of the random forest (RF) model based on the gut microbiome was 86.54%. Nevertheless, the accuracy of SNP was 92.31% in identifying CRC from AA. In conclusion, using microbial SNP was the best method to identify CRC, it was superior to using the gut microbiome, and it could provide new targets for CRC screening. IMPORTANCE There are differences in characteristic microorganisms between AA and CRC. However, current studies have indicated that bacteriological analysis cannot identify CC from AA, and thus, we wondered if there were some other targets that could be used to identify CRC from AA in the gut microbiome. The differences of SNPs in the gut microbiota of intraindividuals were significantly smaller than those of interindividuals. In addition, compared with intestinal microbes, SNP was less affected by time with certain stability. It was discovered that microbial SNP was better than the gut microbiome for identifying CRC from AA. Therefore, screening characteristic microbial SNP could provide a new research direction for identifying CRC from AA.

Shanghai, one of China’s largest metropolises, faces significant environmental pollution challenges due to rapid economic development. Suburban areas of Shanghai are affected by both long-distance transport and local sources of pollutants. This study conducted an integrated analysis that links health-risk assessment of heavy metals and source apportionment of atmospheric constituents to distinguish the contributions of emission sources and the major sources of health risks. Source-apportionment analysis revealed that secondary sources had the greatest contribution to the local pollutants, indicating the significant influence of peripheral and long-distance transport. Health-risk assessment of Cr, Ni, As, and Cd revealed that local residents were exposed to respiratory health risks, in which Cr is the major contributor. This health risk was primarily associated with emissions from nearby industry-related sources. Our study highlights the significant effects of both long-distance transport and local source emissions on atmospheric composition and human health in large urban agglomerations. The findings can inform future efforts to develop more precise emission-reduction strategies and policy improvements to mitigate environmental pollution and protect public health.

Abstract Circulating tumor cells are tumor cells with high vitality and high metastatic potential that invade and shed into the peripheral blood from primary solid tumors or metastatic foci. Due to the heterogeneity of tumors, it is difficult for high-throughput sequencing analysis of tumor tissues to find the genomic characteristics of low-abundance tumor stem cells. Single-cell sequencing of circulating tumor cells avoids interference from tumor heterogeneity by comparing the differences between single-cell genomes, transcriptomes, and epigenetic groups among circulating tumor cells, primary and metastatic tumors, and metastatic lymph nodes in patients' peripheral blood, providing a new perspective for understanding the biological process of tumors. This article describes the identification, biological characteristics, and single-cell genome-wide variation in circulating tumor cells and summarizes the application of single-cell sequencing technology to tumor typing, metastasis analysis, progression detection, and adjuvant therapy.

Pregnancy-induced hypertension (PIH) is one of the most common pregnancy complications that seriously affects the mother and fetus. The incidence of PIH is higher in pregnancies conceived after assisted reproductive technology (ART) than in spontaneous pregnancies; thus, exploring potential serum biomarkers before PIH onset is of great significance for effective early prediction and prevention of PIH in the ART population. Cytokines are involved in the inflammatory response and immune regulation, which play an essential role in the pathogenesis of PIH. A description of the cytokine profile in the first trimester of pregnancy could help identify new diagnostic tools and develop targeted therapies for PIH in the ART population. The concentrations of classical predictive markers for PIH and another 48 cytokines were measured in the first-trimester pregnancy serum samples from 33 PIH patients and 33 matched normotensive controls (NC), both of whom conceived after ART treatment. The measured values were compared and analyzed between NC and PIH, followed by comprehensive bioinformatic analysis and logistic regression analysis. There was no significant difference in classical predictive markers, including Activin A, PlGF, sFLT1 (VEGFR), and sFLT1/PlGF, between the PIH and NC groups (P > 0.05), while 29 cytokines were significantly lower in the PIH group than in the NC group (P < 0.05). Logistic regression analysis revealed that 17 cytokines (IL-2Rα, M-CSF, IL-6, IL-2, β-NGF, IL-7, IL-12 (p70), SCF, IL-10, IL-9, MIG, GM-CSF, LIF, IL-1α, MCP-3, IL-4, and HGF) in the first-trimester pregnancy serum were significantly negatively correlated with the subsequent onset of PIH. With the top 3 cytokines (IL-7, MIG, and SCF) of receiver operating characteristic (ROC) analysis, we constructed an efficient multifactor combined detection and prediction model for PIH in ART pregnancy. Classical early predictors for hypertensive disorder complicating pregnancy cannot distinguish PIH from their normal peers in ART pregnancy. In comparison, the description of the cytokine profile in the first trimester of pregnancy enables us to distinguish high-risk ART pregnancy for PIH, permitting enough time for PIH prevention therapy. The cytokine profile we described also provides immunological insight into the further mechanistic exploration of PIH.

Abstract Long non-coding RNAs (lncRNAs) are a group of non-protein coding RNAs with a length of more than 200 bp. The lncRNA taurine up-regulated gene 1 (TUG1) is abnormally expressed in many human malignant cancers, where it acts as a competitive endogenous RNA (ceRNA), regulating gene expression by specifically sponging its corresponding microRNAs. In the present review, we summarised the current understanding of the role of lncRNA TUG1 in cancer cell proliferation, metastasis, angiogenesis, chemotherapeutic drug resistance, radiosensitivity, cell regulation, and cell glycolysis, as well as highlighting its potential application as a clinical biomarker or therapeutic target for malignant cancer. This review provides the basis for new research directions for lncRNA TUG1 in cancer prevention, diagnosis, and treatment.

Background: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective: To analyze the characteristic microbes in AA. Methods: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing. Highlights: The bacteria (including Prevotella sp900557255, Alistipes putredinis, Megamonas funiformis, etc.) and viruses (including Lilyvirus, Felixounavirus, and Drulisvirus, etc.) existed differences in AA. And there were correlations between AA and basic information, lipid index and serological index. Prediction models based on bacteria and viruses were established to distinguish AA, and the accuracy reached 87.27% and 83.33%. A new typing method was established based on bacteria and viruses to divide gut microbes into 4 subtypes. Prediction models after typing had higher accuracy (100% in B1V1, 96.34% in B1V2, 100% in B2V1, 98.46% in B2V2). [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five‐year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. Review: This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. Conclusion: Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation. [ABSTRACT FROM AUTHOR]

In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti‐tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria‐based drug delivery systems for anti‐tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria‐based drug systems. [ABSTRACT FROM AUTHOR]

Abstract Background Immune escape is an immunological mechanism underlying tumorigenesis, and T cells play an important role in this process. In this study, immune-related genes were evaluated in tumor-infiltrating CD4+ and CD8+ T cells in colon cancer. Methods ESTIMATE was used to calculate stromal and immune scores for tumor datasets downloaded from The Cancer Genome Atlas–Colon Cancer (COAD). Differentially expressed genes (DEGs) between samples with high and low stromal and immune scores were screened, followed by a functional enrichment analysis of the overlapping DEGs. The DEGs related to CD4+ and the CD8+ T cells were then screened. Predicted miRNA–mRNA and lncRNA–miRNA pairs were used to construct a competing endogenous RNA (ceRNA) network. Furthermore, chemical–gene interactions were predicted for genes in the ceRNA network. Kaplan–Meier survival curves were also plotted. Results In total, 83 stromal-related DEGs (5 up-regulated and 78 down-regulated) and 1270 immune-related DEGs (807 up-regulated and 293 down-regulated genes) were detected. The 79 overlapping DEGs were enriched for 39 biological process terms. Furthermore, 79 CD4+ T cell-related genes and 8 CD8+ T cell-related genes, such as ELK3, were screened. Additionally, ADAD1 and DLG3, related to CD4+ T cells, were significantly associated with the prognosis of patients with colon cancer. The chr22-38_28785274–29,006,793.1–miR-106a-5p-DDHD1 and chr22-38_28785274–29,006,793.1–miR-4319-GRHL1 axes obtained from CD4+ and CD8+ T cell-related ceRNAs were identified as candidates for further studies. Conclusion ELK3 is a candidate immune-related gene in colon cancer. The chr22-38_28785274–29,006,793.1–miR-106a-5p-DDHD1 and chr22-38_28785274–29,006,793.1–miR-4319-GRHL1 axes may be related to CD4+ and CD8+ T cell infiltration in colon cancer.

Pancreatic cancer is one of the most lethal cancers and its prognosis is extremely poor. Clarification of molecular mechanisms and identification of prognostic biomarkers are urgently needed. Though we previously found that LGMN was involved in pancreatic carcinoma progression, the upstream regulation of LGMN remains unknown. We used reliable software to search for the potential transcription factors that may be related with LGMN transcription, we found that ELK1 could be a new regulator of LGMN transcription that binded directly to the LGMN promoter. Moreover, knocking down of ELK1 reduced pancreatic cancer cells proliferation, invasion and survival, while LGMN restored the malignancy of pancreatic cancer in vitro and in vivo. Overexpression of ELK1 further increased cancer cells proliferation, invasion and survival. Clinically, ELK1 and LGMN were positively correlated with clinical stage, degree of differentiation and Lymph node infiltration. ELK1 and LGMN were identified as independent prognostic factors for overall survival. The patients with low expression of ELK1/LGMN survived an average of 29.65 months, whereas those with high expression of ELK1/LGMN survived an average of 16.67 months. In conclusive, our results revealed a new mechanism by which ELK1 promoted the progression of pancreatic cancer via LGMN and conferred poor prognosis.

Objective: This study was conducted in order to construct a competitive endogenous RNA (ceRNA) network to screen RNA that plays an important role in colon cancer and to construct a model to predict the prognosis of patients.Methods: The gene expression data of colon cancer were downloaded from the TCGA database. The difference was analyzed by the R software and the ceRNA network was constructed. The survival-related RNA was screened out by combining with clinical information, and the prognosis model was established by lasso regression. CIBERSORT was used to analyze the infiltration of immune cells in colon cancer, and the differential expression of immune cells related to survival was screened out by combining clinical information. The correlation between RNA and immune cells was analyzed by lasso regression. PCR was used to verify the expression of seven RNAs in colon cancer patients with different prognoses.Results: Two hundred and fifteen lncRNAs, 357 miRNAs, and 2,955 mRNAs were differentially expressed in colon cancer. The constructed ceRNA network contains 18 lncRNAs, 42 miRNAs, and 168 mRNAs, of which 18 RNAs are significantly related to survival. Through lasso analysis, we selected seven optimal RNA construction models. The AUC value of the model was greater than 0.7, and there was a significant difference in the survival rate between the high- and low-risk groups. Two kinds of immune cells related to the prognosis of patients were screened out. The results showed that the expression of seven RNA markers in colon cancer patients with different prognoses was basically consistent with the model analysis.Conclusion: We have established the regulatory network of ceRNA in colon cancer, screened out seven core RNAs and two kinds of immune cells, and constructed a comprehensive prognosis model of colon cancer patients.

ObjectiveThis study aimed to characterize the tumor-infiltrating T cells in moderately differentiated colorectal cancer.MethodsUsing single-cell RNA sequencing data of isolated 1632 T cells from tumor tissue and 1252 T cells from the peripheral blood of CRC patients, unsupervised clustering analysis was performed to identify functionally distinct T cell populations, followed by correlations and ligand-receptor interactions across cell types. Finally, differential analysis of the tumor-infiltrating T cells between colon cancer and rectal cancer were carried out.ResultsA total of eight distinct T cell populations were identified from tumor tissue. Tumor-Treg showed a strong correlation with Th17 cells. CD8+TRM was positively correlated with CD8+IEL. Seven distinct T cell populations were identified from peripheral blood. There was a strong correlation between CD4+TN and CD4+blood-TCM. Colon cancer and rectal cancer showed differences in the composition of tumor-infiltrating T cell populations. Tumor-infiltrating CD8+IEL cells were found in rectal cancer but not in colon cancer, while CD8+ TN cells were found in the peripheral blood of colon cancer but not in that of rectal cancer. A larger number of tumor-infiltrating CD8+ Tex (88.94%) cells were found in the colon cancer than in the rectal cancer (11.06%). The T cells of the colon and rectal cancers showed changes in gene expression pattern.ConclusionsWe characterized the T cell populations in the CRC tumor tissue and peripheral blood.