Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis.

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms.

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Establishment and comprehensive characterization of a novel dark-reared zebrafish model for myopia studies.

Myopia is predicted to impact approximately 5 billion people by 2050, necessitating mechanistic understanding of its development. Myopia results from dysregulated genetic mechanisms of emmetropization, caused by over-exposure to aberrant visual environments; however, these genetic mechanisms remain unclear. Recent human genome-wide association studies have identified a range of novel myopia-risk genes. To facilitate large-scale in vivo mechanistic examination of gene-environment interactions, this study aims to establish a myopia model platform that allows efficient environmental and genetic manipulations. We established an environmental zebrafish myopia model by dark-rearing. Ocular biometrics including relative ocular refraction were quantified using optical coherence tomography images. Spatial vision was assessed using optomotor response (OMR). Retinal function was analyzed via electroretinography (ERG). Myopia-associated molecular contents or distributions were examined using RT-qPCR or immunohistochemistry. Our model produces robust phenotypic changes, showing myopia after 2 weeks of dark-rearing, which were recoverable within 2 weeks after returning animals to normal lighting. 2-week dark-reared zebrafish have reduced spatial-frequency tuning function. ERG showed reduced photoreceptor and bipolar cell function (a- and b-waves) after only 2 days of dark-rearing, which worsened after 2 weeks of dark-rearing. We also found dark-rearing-induced changes to expression of myopia-risk genes, including egr1, vegfaa, vegfab, rbp3, gjd2a and gjd2b, inner retinal distribution of EFEMP1, TIMP2 and MMP2, as well as transiently reduced PSD95 density in the inner plexiform layer. Coupled with the gene editing tools available for zebrafish, our environmental myopia model provides an excellent platform for large-scale investigation of gene-environment interactions in myopia development.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication.

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

A novel regulatory mechanism network mediated by lncRNA TUG1 that induces the impairment of spiral artery remodeling in preeclampsia.

Preeclampsia (PE) is associated with maternal and fetal perinatal morbidity and mortality, which brings tremendous suffering and imposes an economic burden worldwide. The failure of uterine spiral artery remodeling may be related to the abnormal function of trophoblasts and lead to the occurrence and progression of PE. Aberrant expression of long non-coding RNAs (lncRNAs) is involved in the failure of uterine spiral artery remodeling. However, the regulation of lncRNA expression in PE is poorly characterized. Here, we reported that hypoxia-induced microRNA (miR)-218 inhibited the expression of lncRNA TUG1 by targeting FOXP1. Further RNA sequencing and mechanism analysis revealed that silencing of TUG1 increased the expression of DNA demethylase TET3 and proliferation-related DUSP family, including DUSP2, DUSP4, and DUSP5, via binding to SUV39H1 in the nucleus. Moreover, TUG1 modulated the DUSP family in vitro through a TET3-mediated epigenetic mechanism. Taken together, our results unmask a new regulatory network mediated by TUG1 as an essential determinant of the pathogenesis of PE, which regulates cell growth and possibly the occurrence and development of other diseases.

Prediction of the Number of Cumulative Pulses Based on the Photon Statistical Entropy Evaluation in Photon-Counting LiDAR.

Photon-counting LiDAR encounters interference from background noise in remote target detection, and the statistical detection of the accumulation of multiple pulses is necessary to eliminate the uncertainty of responses from the Geiger-mode avalanche photodiode (Gm-APD). The cumulative number of statistical detections is difficult to select due to the lack of effective evaluation of the influence of the background noise. In this work, a statistical detection signal evaluation method based on photon statistical entropy (PSE) is proposed by developing the detection process of the Gm-APD as an information transmission model. A prediction model for estimating the number of cumulative pulses required for high-accuracy ranging with the background noise is then established. The simulation analysis shows that the proposed PSE is more sensitive to the noise compared with the signal-to-noise ratio evaluation, and a minimum PSE exists to ensure all the range detections with background noise are close to the true range with a low and stable range error. The experiments demonstrate that the prediction model provides a reliable estimation of the number of required cumulative pulses in various noise conditions. With the estimated number of cumulative pulses, when the signal photons are less than 0.1 per pulse, the range accuracy of 4.1 cm and 5.3 cm are obtained under the background noise of 7.6 MHz and 5.1 MHz, respectively.

Burden of early hospitalization after simultaneous liver-kidney transplantation: Results from the US Multicenter SLKT Consortium.

The burden of early hospitalization (within 6 months) following simultaneous liver-kidney transplant (SLKT) is not known. We examined risk factors associated with early hospitalization after SLKT and their impact on patient mortality conditional on 6-month survival. We used data from the US Multicenter SLKT Consortium cohort study of all adult SLKT recipients between 2002 and 2017 who were discharged alive following SLKT. We used Poisson regression to model rates of early hospitalizations after SLKT. Cox regression was used to identify risk factors associated with mortality conditional on survival at 6 months after SLKT. Median age (N = 549) was 57.7 years (interquartile range [IQR], 50.6-63.9) with 63% males and 76% Whites; 33% had hepatitis C virus, 20% had non-alcohol-associated fatty liver disease, 23% alcohol-associated liver disease, and 24% other etiologies. Median body mass index (BMI) and Model for End-Stage Liver Disease-sodium scores were 27.2 kg/m2 (IQR, 23.6-32.2 kg/m2 ) and 28 (IQR, 23-34), respectively. Two-thirds of the cohort had at least one hospitalization within the first 6 months of SLKT. Age, race, hospitalization at SLKT, diabetes mellitus, BMI, and discharge to subacute rehabilitation (SAR) facility after SLKT were independently associated with a high incidence rate ratio of early hospitalization. Number of hospitalizations within the first 6 months did not affect conditional survival. Early hospitalizations after SLKT were very common but did not affect conditional survival. Although most of the risk factors for early hospitalization were nonmodifiable, discharge to SAR after initial SLKT was associated with a significantly higher incidence rate of early hospitalization. Efforts and resources should be focused on identifying SLKT recipients at high risk for early hospitalization to optimize their predischarge care, discharge planning, and long-term follow-up.

An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar.

Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.

Neurocognitive effects of atypical antipsychotics in patients with first-episode schizophrenia.

Introduction: Cognitive impairment is a core feature of schizophrenia. The effects of atypical antipsychotics on the cognitive functions of patients with first-episode schizophrenia have not been comprehensively investigated so far. This study aims to compare neurocognitive effects of risperidone, olanzapine, and aripiprazole for first-episode schizophrenia.Methods: The study was a multicenter, randomized, open-label clinical trial. 546 patients were randomly divided into three medication groups, and followed up for 1 year. Cognitive performance was evaluated with a neuropsychological test battery. The Clinical trials.gov ID of the study is NCT01057849.Results: At 6 months, treatment resulted in significant improvements in all three groups in most cognitive domains except verbal learning and memory. At 12 months, three treatment groups had further improvements in three cognitive domains, but visual learning and memory performance dropped back to baseline.Conclusion: All three atypical antipsychotics tested in the study can potentially improve cognitive performance in first-episode schizophrenia, but no significant difference in the degree of improvement was found between drugs.

Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition.

Non-line-of-sight imaging has attracted more attentions for its wide applications. Even though ultrasensitive cameras/detectors with high time-resolution are available, current back-projection methods are still powerless to acquire a satisfying reconstruction of multiple hidden objects due to severe aliasing artifacts. Here, a novel back-projection method is developed to reconstruct multiple hidden objects. Our method considers decomposing all the ellipsoids in a confidence map into several "clusters" belonging to different objects (namely "ellipsoid mode decomposition"), and then reconstructing the objects individually from their ellipsoid modes by filtering and thresholding, respectively. Importantly, the simulated and experimental results demonstrate that this method can effectively eliminate the impacts of aliasing artifacts and exhibits potential advantages in separating, locating and recovering multiple hidden objects, which might be a good base for reconstructing complex non-line-of-sight scenes.

Richardson-Lucy deconvolution of time histograms for high-resolution non-line-of-sight imaging based on a back-projection method.

Non-line-of-sight (NLoS) imaging is a newly developed technique with widespread applications in recent years. At present, it is difficult for NLoS imaging to achieve a high-resolution reconstruction with an existing back-projection method. We propose a novel approach based on the Richardson-Lucy deconvolution of time histograms for high-resolution NLoS imaging. In our approach, the backscattering photons from a line-of-sight scene, often regarded as adverse factors, are used favorably as priors for the deconvolution of time histograms. All deconvoluted time histograms are back projected into the space to form a high-resolution NLoS reconstruction. The simulated and experimental results with the approach demonstrated that the reconstructions of NLoS objects are indeed with high resolution, even under a relatively high noise level.

Neural Endophenotype Assessment in Zebrafish Larvae Using Optomotor and ZebraBox Locomotion Assessment.

Due to the highly conserved genetics across the central nervous system, the easily probed visual system can act as an endophenotype for assessing neurological function. Here, we describe a psychophysics approach to assess visually driven swimming behavior in the high-throughput zebrafish genetic model system. We use the optomotor response test together with general locomotion behavior to assess neural processing while excluding motor defects related to muscle function.

Identification and extraction of cementation patterns in sand modified by MICP: New insights at the pore scale.

Microbially induced calcium carbonate precipitation (MICP) is an environmentally friendly technology that improves soil permeability resistance through biocementation. In this study, 2D microscopic analysis and 3D volume reconstruction were performed on river sand after 24 cycles of bio-treatment based on stacked images and computed tomography (CT) scanning data, respectively, to extract biocementation patterns between particles. Based on the mutual validation findings of the two techniques, three patterns in the biocemented sand were identified as G-C-G, G-C, and G-G. Specifically, 2D microscopic analysis showed that G-C-G featured multi-particle encapsulation and bridging, with a pore filling ratio of 81.2%; G-C was characterized by locally coated particle layers, with a pore filling ratio of 19.7%; and the G-G was marked by sporadic filling of interparticle pores, with a pore filling ratio of 11.7%. G-C-G had the best cementation effect and permeability resistance (effective sealing rate of 68.5%), whereas G-C (effective sealing rate of 2.4%) had a relatively minor contribution to pore-filling and flow sealing. 3D volume reconstruction showed that G-C-G had the highest pore filling rate, followed by G-G and G-C. The average filling ratios of area and volume for G-C-G were 83.979% and 77.257%, respectively; for G-G 20.360% and 23.600%; and for G-C 11.545% and 11.250%. The analysis of the representative element volume (REV) was conducted, and the feasibility and reliability of the micro-scale pattern extraction results were confirmed to guide the analysis of macro-scale characteristics. The exploration of the effectiveness of cementation patterns in fluid sealing provides valuable insights into effective biocementation at the pore scale of porous media, which may inspire future research.

Gastrointestinal bleeding following Heartmate 3 left ventricular assist device implantation: The Michigan Bleeding Risk Model.

BACKGROUND: Gastrointestinal bleeding (GIB) results in frequent hospitalizations and impairs quality of life in durable left ventricular assist device (LVAD) recipients. Anticipation of these events before implantation could have important implications for patient selection and management. METHODS: The study population included all adult HeartMate 3 (HM3) primary LVAD recipients enrolled in the STS Intermacs registry from January 2017 to December 2020. Using multivariable modeling methodologies, we investigated the relationships between preimplantation characteristics and postimplant bleeding, bleeding and death, and additional bleeding episodes on subsequent bleeding episodes and created a risk score to predict the likelihood of post-LVAD GIB based solely on preimplantation factors. RESULTS: Of 6,425 patients who received an HM3 LVAD, 1,010 (15.7%) patients experienced GIB. Thirteen preimplantation factors were independent predictors of post-LVAD GIB. A risk score was created from these factors and calculated for each patient. By 3 years postimplant, GIB occurred in 11%, 26%, and 43% of low-, medium- and high-risk patients, respectively. Experiencing 1 post-LVAD GIB event was associated with an increased risk for further GIB events, with 33.9% of patients experiencing at least 1 recurrence. While post-LVAD GIB was associated with mortality, there was no relationship between number of GIB events and death. CONCLUSIONS: The Michigan Bleeding Risk Model is a simple tool, which facilitates the prediction of post-LVAD GIB in HM3 recipients using 13 preimplant variables. The implementation of this tool may help in the risk stratification process and may have therapeutic and clinical implications in HM3 LVAD recipients.

Bridging Chronic Inflammation and Digestive Cancer: The Critical Role of Innate Lymphoid Cells in Tumor Microenvironments.

The incidence and mortality of digestive system-related cancers have always been high and attributed to the heterogeneity and complexity of the immune microenvironment of the digestive system. Furthermore, several studies have shown that chronic inflammation in the digestive system is responsible for cancer incidence; therefore, controlling inflammation is a potential strategy to stop the development of cancer. Innate Lymphoid Cells (ILC) represent a heterogeneous group of lymphocytes that exist in contrast to T cells. They function by interacting with cytokines and immune cells in an antigen-independent manner. In the digestive system cancer, from the inflammatory phase to the development, migration, and metastasis of tumors, ILC have been found to interact with the immune microenvironment and either control or promote these processes. The conventional treatments for digestive tumors have limited efficacy, therefore, ILC-associated immunotherapies are promising strategies. This study reviews the characterization of different ILC subpopulations, how they interact with and influence the immune microenvironment as well as chronic inflammation, and their promotional or inhibitory role in four common digestive system tumors, including pancreatic, colorectal, gastric, and hepatocellular cancers. In particular, the review emphasizes the role of ILC in associating chronic inflammation with cancer and the potential for enhanced immunotherapy with cytokine therapy and adoptive immune cell therapy.