A miRNA-7704/IL2RB/AKT feedback loop regulates tumorigenesis and chemoresistance in ovarian cancer.

Ovarian cancer is one of the most common gynecological tumors worldwide. Despite the availability of multiple treatments for ovarian cancer, its resistance to chemotherapy remains a significant challenge. miRNAs play crucial roles in the initiation and progression of cancer by affecting processes such as differentiation, proliferation, and chemoresistance. According to microarray and qPCR analyses, miR-7704 is significantly downregulated in cisplatin-resistant cells compared to parental cells. In this study, we found that miR-7704 inhibited the proliferation and promoted cisplatin sensitivity of ovarian cancer cells in vitro and in vivo. Moreover, ectopic expression of miR-7704 had the same effect as IL2RB knockdown. Further mechanistic studies revealed that miR-7704 played an inhibitory role by regulating IL2RB expression to inactivate the AKT signaling pathway. Furthermore, IL2RB reversed the miR-7704 mediated resistance to cisplatin in ovarian cancer. Based on these findings, miR-7704 and IL2RB show the potential as novel therapeutic targets for ovarian cancer.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Towards a Green Equal City: Measuring and matching the supply-demand of green exposure in urban center.

Exposure to green environments is crucial for human health. However, urbanization has reduced the contact of urban residents with natural environments, causing a mismatch between the supply and demand for green exposure. Research in this field is hindered by the lack of long-term, reliable data sources and methodologies, leading to insufficient consideration of temporal variations in green exposure. This study presented a comprehensive methodology for assessing green exposure at a fine scale utilizing satellite images for urban tree canopy identification. We conducted a case study in the core area of Beijing from 2010 to 2020 and examined the effects of urban renewal and alleviation efforts. The results revealed a slight decrease in green exposure for the elderly over the decade, with minimal changes in equity. In contrast, green exposure for children has increased, with increasing inequality. Moreover, urban renewal has improved green exposure for nearly half of the low-supply blocks. However, a significant mismatch was observed between supply and demand for blocks with increased demand but limited supply. This study enhances the assessment of green exposure and provides guidance for planning and constructing a "Green Equal City".

Promoting proliferation and tumorigenesis of breast cancer: KCND2's significance as a prognostic factor.

In recent years, the potassium voltage-gated channel subfamily D (KCND) channels, particularly KCND2 (also known as Kv4.2), have been suggested to play a role in a variety of cancers, but their role in breast cancer has not yet been revealed. We analyzed RNA sequencing data from The Cancer Genome Atlas database and the Genotype-Tissue Expression database to investigate the differential expression of KCND2 in breast cancer and normal breast tissue. In addition, we leveraged GO and KEGG analysis techniques to gain a better understanding of the potential functional enrichment of 500 genes related to KCND2. Our findings were validated using collected tissue samples and clinical data from hospitals showed that KCND2 is a crucial independent factor in the prognosis of breast cancer patients. The higher the expression of KCND2, the shorter the survival time of breast cancer patients. Colony formation assay confirmed that KCND2 promotes the proliferation of breast cancer cells, whereas transwell assay and wound healing assay verified that KCND2 promoted breast cancer invasion and migration. In addition, 5-Ethynyl-2'-deoxyuridine (EdU) and flow cytometry revealed that KCND2 affected the cycle changes of breast cancer cells and contributed to the G1/S phase transition of breast cancer cells. Overall, our study demonstrates that KCND2 holds a promising potential as a significant target for breast cancer diagnosis and therapy.

A climate-water quality assessment framework for quantifying the contributions of climate change and human activities to water quality variations.

Water quality safety has attracted global attention and is closely related to the development of the social economy and human health. It is widely recognized that climate change and human activities significantly affect water quality changes. Therefore, quantifying the contributions of factors that drive long-term water quality changes is crucial for effective water quality management. Here, we built a climate-water quality assessment framework (CWQAF) based on climate-water quality response coefficients and trend analysis methods, to achieve this goal. Our results showed that the water quality improved significantly by 4.45%-20.54% from 2011 to 2020 in the Minjiang River basin (MRB). Human activities (including the construction of ecological projects, stricter discharge measures, etc.) were the main driving factors contributing 65%-77% of the improvement effect. Notably, there were differences in the contributions of human activities to water quality parameter changes, such as DO (increase (I): 0.12 mg/L, human contribution (HC): 66.8%), CODMn (decrease (D): 0.71 mg/L, HC: 67.2%), BOD5 (D: 1.10 mg/L, HC: 77.7%), CODCr (D: 4.20 mg/L, HC: 81.2%), TP (D: 0.13 mg/L,HC: 72.8%) and NH3-N (D: 0.40 mg/L, HC: 63.0%). Climate change explained 23%-35% of the variation in water quality. The water quality response to climate change was relatively significant with precipitation. For example, the downstream region was more susceptible to climate change than was the upstream region, as the downstream movement of precipitation centers strengthened the process of climatic factors affecting water quality changes in the MRB. Generally, although human activities were the main driving factor of water quality changes at the basin scale, the contribution of climate change could not be ignored. This study provided a manageable framework for the quantitative analysis of the influence of human activities and climate change on water quality to enable more precise and effective water quality management.

The roles, controversies, and combination therapies of autophagy in lung cancer.

Lung cancer is one of the leading causes of death among men and women worldwide. The disease initially has a silent phenotype, which leads to the progression of the disease and ultimately the lack of proper response to routine treatments. Autophagy, known as an intracellular "recycle bin" for the degradation of defective proteins and molecules, is one of the mechanisms that has been considered in the context of cancer in recent years. This study aims to provide a comprehensive review of published articles on autophagy in the context of lung cancer to have a complete view of the role of autophagy in lung cancer and its possible treatments. PubMed, Scopus, and Google Scholar were searched until June 15 to find related articles. No specific search filters or restrictions were applied. The results were entered into reference management software for aggregation and management. The full text of all articles was screened and studied. In conclusion, studies on the exact function of autophagy in lung cancer are contradictory, but what can be concluded from a review of literature on lung cancer is that targeting autophagy combined with traditional routine therapies such as chemotherapy, especially in advanced stages of lung cancer, can be an effective anticancer approach.

SAFARI: shape analysis for AI-segmented images.

BACKGROUND: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. RESULTS: We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I-IV lung cancer and another case study on 61 glioblastoma patients. CONCLUSIONS: SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .

Immune checkpoint inhibitors plus anlotinib versus anlotinib alone as third-line treatment in advanced non-small-cell lung cancer: a retrospective study.

Aim: This study was conducted to evaluate the efficacy of immune checkpoint inhibitors (ICIs) plus anlotinib versus anlotinib alone to provide guidance for clinical treatment of non-small-cell lung cancer. Patients & methods: The records of 139 patients with advanced non-small-cell lung cancer who received at least one dose of ICIs plus anlotinib (IA group) or single-agent anlotinib (AA group) were retrospectively reviewed. The efficacy of the treatments, survival outcomes and adverse events were analyzed. The primary end point was investigator-assessed progression-free survival (PFS). Result: The IA group had a significantly prolonged median PFS (mPFS: 5.8 vs 4.2 months; p = 0.022) compared with the AA group (hazard ratio: 0.68; 95% CI: 0.68-0.97). In patients with brain metastases, the IA group exhibited improved efficacy (mPFS: 6.0 vs 3.8 months; p = 0.034) compared with the AA group (hazard ratio: 0.49; 95% CI: 0.23-1.05). Conclusion: ICIs plus anlotinib significantly improved efficacy compared with anlotinib alone and showed substantial potential for the control of intracranial lesions.

Antitumor Activity and Potential Mechanism of Novel Fullerene Derivative Nanoparticles.

The development of novel nanoparticles as a new generation therapeutic drug platform is an active field of chemistry and cancer research. In recent years, fullerene nanoparticles have received extensive attention due to their unique physical and chemical properties. Properly modified fullerene nanoparticles have excellent biocompatibility and significant anti-tumor activity, which makes them have broad application prospects in the field of cancer therapy. Therefore, understanding the anti-tumor mechanism of fullerene nanoparticles is of great significance for the design and development of anti-tumor drugs with low toxicity and high targeting. This review has focused on various anti-tumor mechanisms of fullerene derivatives and discusses their toxicity and their distribution in organisms. Finally, the review points out some urgent problems that need solution before fullerene derivatives as a new generation of anti-tumor nano-drug platform enter clinical research.

A Novel Blockchain Framework for Industrial IoT Edge Computing.

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.

TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data.

Motivation: The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC. Results: We develop TCGA-assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research. Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. Availability and implementation: http://www.compgenome.org/TCGA-Assembler/ or https://github.com/compgenome365/TCGA-Assembler-2. Contact: zhuyitan@gmail.com or koaeraser@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Clustering distributions with the marginalized nested Dirichlet process.

We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene-gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. It allows simulation exact inference, as opposed to a truncated Dirichlet process approximation. More importantly, the construction highlights the nature of the nested Dirichlet process as a nested partition of experimental units. We apply the proposed model to inference on clustering genes related to DNA mismatch repair (DMR) by the distribution of gene-gene interactions with other genes. Gene-gene interactions are recorded as coefficients in an auto-logistic model for the co-expression of two genes, adjusting for copy number variation, methylation and protein activation. These coefficients are extracted from an online database, called Zodiac, computed based on The Cancer Genome Atlas (TCGA) data. We compare results with a variation of k-means clustering that is set up to cluster distributions, truncated NDP and a hierarchical clustering method. The proposed inference shows favorable performance, under simulated conditions and also in the real data sets.

Antiproliferative and apoptosis inducing effect of essential oil extracted from Cyrtomium fortumei (J.) Smith leaves.

Cyrtomium fortumei (J.) Smith is an endemic species in China, which has been proved to be an important Chinese herbal medicine. However, chemical composition and bioactivity of essential oil (EO) of C. fortumei (J.) Smith leaves remain unclear. In present study, we isolated EO from the plant by supercritical carbon dioxide extraction assay (SFE-CO2), and investigated on cancer cells MGC-803, MCF-7, BGC-823, Bcap-37, A375, and A549 in vitro by MTT assay. 26 compounds were identified by GC-MS analysis, and the EO showed significant antitumor activities against MGC-803, Bcap-37, and A549 cancer cell lines (IC50 values ranging from 0.15 to 0.24 mg/mL), and the activities of its main component were also studied. Subsequent fluorescence staining and flow cytometry analysis indicated that the EO could induce apoptosis in MGC-803, Bcap-37, and A549 cell lines, and the apoptosis ratios reached 26.44 % after 48 h of treatment at 0.15 mg/mL in MGC-803 cells. Caspase 3 activity in MGC-803 cells was also determined when the cells treated with the oil, and the activity of caspase 3 enzyme was increased compared to the control. This study suggests that the EO isolated from C. fortumei (J.) Smith could inhibit the growth of human carcinoma cells, and it could induce apoptosis of cancer cells.

Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals.

BACKGROUND: While the possible sources underlying the so-called 'missing heritability' evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits. RESULTS: Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed. CONCLUSIONS: We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.

Research application of session-RPE in monitoring the training load of elite endurance athletes.

Purpose: TRIMP and sRPE are both representative indicators of training load(TL), and the correlation between two has been widely demonstrated across various sports. The aim of this study was to investigate the reliability of sRPE-TRIMP correlation across different intensities/duration of training in cross-country skiing, and whether sRPE can serve as an validity supplement to TRIMP data in cases of lost heart rate data. Method: 10 athletes were used as the experimental objects. The intensity, duration and RPE of 273 different types of training sessions were collected, and statistical methods were used for data analysis. Results: 1. There was a significant correlation between sRPE and TRIMP (r = 0.68, p < 0.05), but the correlation differs among the LIT, MIT and HIT groups (r = 0.70, 0.46, r = 0.31, p < 0.05) 2. sRPE-TRIMP correlation among three different time duration in the LIT group (0-60 min, 60-120 min and 120-180 min), are all highly significant (r = 0.70, 0.67, 0.69, p < 0.05) and the LRsRPE-TRIMP of 3 duration have no significant differences (chow test, p > 0.05). 3. The difference in actual training duration between samples was the main reason for the difference in the application effect of sRPE, because the actual training duration ratio of LIT was 89.7 ± 16.4%; MIT, 98.5 ± 6.2%; and HIT, 94.4 ± 13.5%. Conclusion: 1. The linear relationship between sRPE and TRIMP (LRsRPE-TRIMP) is more significant in LIT compared to that in MIT and HIT. 2. Variations in the duration of LIT sessions do not affect the consistency of the relationship between sRPE and TRIMP. 3. Discrepancies between actual and planned training durations directly impact the significance of the LRsRPE-TRIMP.

The association between telomere length and blood lipids: a bidirectional two-sample Mendelian randomization study.

Background: Observational studies suggest an association between telomere length (TL) and blood lipid (BL) levels. Nevertheless, the causal connections between these two traits remain unclear. We aimed to elucidate whether genetically predicted TL is associated with BL levels via Mendelian randomization (MR) and vice versa. Methods: We obtained genetic instruments associated with TL, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-1 (ApoA-1) and apolipoprotein B (ApoB) from large-scale genome-wide association studies (GWASs). The causal relationships between TL and BL were investigated via bidirectional MR, multivariable MR and mediation analysis methods. The inverse variance weighted (IVW) method was employed as the principal methodology, complemented by several other estimators to enhance the robustness of the analysis. Results: In the forward MR analyses, we identified significant positive correlation between genetically predicted TL and the levels of TG (ß=0.04, 95% confidence interval [CI]: 0.01 to 0.06, p = 0.003). In the reverse MR analysis, TG (ß=0.02, 95% CI: 0.01 to 0.03, p = 0.004), LDL-C (ß=0.03, 95% CI: 0.01 to 0.04, p = 0.001) and ApoB (ß=0.03, 95% CI: 0.01 to 0.04, p = 9.71×10-5) were significantly positively associated with TL, although this relationship was not observed in the multivariate MR analysis. The mediation analysis via two-step MR showed no significant mediation effects acting through obesity-related phenotypes in analysis of TL with TG, while the effect of LDL-C on TL was partially mediated by body mass index (BMI) in the reverse direction, with mediated proportion of 12.83% (95% CI: 0.62% to 25.04%). Conclusions: Our study indicated that longer TL were associated with higher TG levels, while conversely, higher TG, LDL-C, and ApoB levels predicted longer TL, with BMI partially mediating these effects. Our findings present valuable insights into the development of preventive strategies and interventions that specifically target TL-related aging and age-related diseases.

Identification of a Group of Therapeutic Targets and Prognostic Biomarker for Triple Negative Breast Cancer.

INTRODUCTION: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Mining differentially expressed genes of TNBC is helpful to explore new therapeutic targets. This study aimed to investigate diagnostic biomarker genes in TNBC compared to normal tissue. Additionally, we explored the functions and prognostic value of these key genes as well as potential targeted drugs that could affect these genes. METHODS: Differential gene expression analysis was conducted using the R software with data from the Gene Expression Omnibus (GEO) database. Then, the identified differentially expressed genes (DEGs) were used to construct a protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. The mRNA expression levels of key genes were analyzed using the UALCAN database with data from The Cancer Genome Atlas (TCGA). Enrichment and survival analyses were performed using R software. In addition, potential compounds showing sensitivity to key genes were identified by gene set cancer analysis (GSCA). RESULTS: Compared with normal tissues, a total of 203 DEGs were upregulated in TNBC. These DEGs participated in various biological processes including nuclear division, microtubule binding, cell cycle, and the p53 signaling pathway. Through the PPI network analysis, ten key genes were identified, among which four genes showed significant correlation with poor progression-free interval (PFI) in patients with TNBC. Moreover, the four survival-related genes were found to act as sensitive therapeutic targets. CONCLUSION: The identified four key genes were considered new biomarkers for diagnosis and prognosis and also potential therapeutic targets for TNBC.

Copper homeostasis and cuproptosis in atherosclerosis: metabolism, mechanisms and potential therapeutic strategies.

Copper is an essential micronutrient that plays a pivotal role in numerous physiological processes in virtually all cell types. Nevertheless, the dysregulation of copper homeostasis, whether towards excess or deficiency, can lead to pathological alterations, such as atherosclerosis. With the advent of the concept of copper-induced cell death, termed cuproptosis, researchers have increasingly focused on the potential role of copper dyshomeostasis in atherosclerosis. In this review, we provide a broad overview of cellular and systemic copper metabolism. We then summarize the evidence linking copper dyshomeostasis to atherosclerosis and elucidate the potential mechanisms underlying atherosclerosis development in terms of both copper excess and copper deficiency. Furthermore, we discuss the evidence for and mechanisms of cuproptosis, discuss its interactions with other modes of cell death, and highlight the role of cuproptosis-related mitochondrial dysfunction in atherosclerosis. Finally, we explore the therapeutic strategy of targeting this novel form of cell death, aiming to provide some insights for the management of atherosclerosis.

Biomagnification and health risks of perflfluoroalkyl acids (PFAAs) in seafood from the Yangtze river estuary of China.

Bioaccumulation and human health risk assessment of Perfluoroalkyl acids (PFAAs) is important for pollutant hazard assessment. In this study, 26 aquatic organisms were collected from the Yangtze River estuary, the PFAAs concentrations in organisms were detected by liquid chromatography-mass spectrometry, and the trophic levels of organisms were constructed using nitrogen isotope analysis. The results showed that Perfluorobutane sulfonate (PFBS) was predominant in organisms with the mean concentration of 6.43 ± 8.21 ng/g ww. The biomagnification of organisms along the food chain was widespread, and the biomagnification factor (BMF) of perfluorooctane sulfonic (PFOS) was the most prominent. Trophic magnifcation factors (TMFs) of PFAAs were estimated in the marine food web, and TMFs >1 were observed in Perfluorodecanoic acid (PFDA), Perfluoroundecanoic acid (PFUnDA), Perfluorododecanoic acid (PFDoDA), and PFOS, indicating the biomagnifcation effects of these 4 individual PFAAs in organisms at Yangtze River estuary. The estimated daily intake (EDI) of PFBS was highest in adolescents aged 6-18 years, with EDIs of 18.9 ng/kg·bw/day for males and 14.0 ng/kg·bw/day for females. The hazard ratio (HR) of PFAAs reported in different age and gender groups were lower than 1.

The Emerging Function and Promise of tRNA-Derived Small RNAs in Cancer.

Fragments derived from tRNA, called tRNA-derived small RNAs (tsRNAs), have attracted widespread attention in the past decade. tsRNAs are widespread in prokaryotic and eukaryotic transcriptome, which contains two main types, tRNA-derived fragments (tRFs) and tRNA-derived stress-inducing RNA (tiRNAs), derived from the precursor tRNAs or mature tRNAs. According to differences in the cleavage position, tRFs can be divided into tRF-1, tRF-2, tRF-3, tRF-5, and i-tRF, whereas tiRNAs can be divided into 5'-tiRNA and 3'-tiRNA. Studies have found that tRFs and tiRNAs are abnormally expressed in a variety of human malignant tumors, promote or inhibit the proliferation and apoptosis of cancer cells by regulating the expression of oncogene, and play an important role in the aggressive metastasis and progression of tumors. This article reviews the biological origins of various tsRNAs, introduces their functions and new concepts of related mechanisms, and focuses on the molecular mechanisms of tsRNAs in cancer, including breast cancer, prostate cancer, colorectal cancer, lung cancer, b-cell lymphoma, and chronic lymphoma cell leukemia. Lastly, this article puts forward some unresolved problems and future research prospects.