Identification and Validation of an Immune-Related lncRNA Signature to Facilitate Survival Prediction in Gastric Cancer

Non-coding RNAs in cancer immunotherapy: A solution to overcome immune resistance?

With the rapid advancement in immunotherapy, cancer immune resistance has become more evident, which demands new treatment approaches to achieve greater efficacy. Non-coding RNAs (ncRNAs) are a heterogeneous group of RNAs that are not translated to proteins but instead regulate different stages of gene expression. Recent studies have increasingly supported the critical role of ncRNAs in immune cell-cancer cell cross-talk, and numerous ncRNAs have been implicated in the immune evasion of cancer cells. Cancer cells take advantage of ncRNAs to modulate several signaling pathways and upregulate the expression of immune checkpoints and anti-inflammatory mediators, thereby dampening the anti-tumor response of M1 macrophages, dendritic cells, cytotoxic T cells, and natural killer cells or potentiating the immunosuppressive properties of M2 macrophages, regulatory T cells, and myeloid-derived suppressive cells. Upregulation of immunosuppressive ncRNAs or downregulation of immunogenic ncNRAs is a major driver of resistance to immune checkpoint inhibitors, cancer vaccines, and other means of cancer immunotherapy, making ncRNAs ideal targets for treatment. In addition, ncRNAs released by cancer cells have been demonstrated to possess prognostic values for patients who undergo cancer immunotherapy. Future clinical trials are urged to consider the potential of ncRNAs in cancer immunotherapy.

Analysis of the factors influencing moderate to poor performance status in patients with cancer after chemotherapy: a cross-sectional study comparing three models

There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate-poor performance status. A convenience sampling method was used. Demographic and clinical data and evaluation results for fatigue, pain, quality of life and Eastern Cooperative Oncology Group status were collected three days after the end of chemotherapy. Decision tree, random forest and logistic regression models were constructed. Ninety-four subjects in the case group had moderate to poor performance status, and 365 subjects in the control group had no or mild activity disorders. The random forest model was the most accurate model. Physical function, total protein, general quality of life within one week before chemotherapy, hemoglobin, pain symptoms and globulin were the main factors. Total protein and hemoglobin levels reflect nutritional status, and globulin levels are an index of liver function. Therefore, physical function, nutritional status, general quality of life and pain symptoms within one week before chemotherapy and liver function can be used to predict moderate-poor performance status. Nurses should pay more attention to patients with poor physical function, poor nutritional status, lower quality of life and pain symptoms after chemotherapy.

Non-coding RNA in tumor-infiltrating regulatory T cells formation and associated immunotherapy

Cancer immunotherapy has exhibited promising antitumor effects in various tumors. Infiltrated regulatory T cells (Tregs) in the tumor microenvironment (TME) restrict protective immune surveillance, impede effective antitumor immune responses, and contribute to the formation of an immunosuppressive microenvironment. Selective depletion or functional attenuation of tumor-infiltrating Tregs, while eliciting effective T-cell responses, represents a potential approach for anti-tumor immunity. Furthermore, it does not disrupt the Treg-dependent immune homeostasis in healthy organs and does not induce autoimmunity. Yet, the shared cell surface molecules and signaling pathways between Tregs and multiple immune cell types pose challenges in this process. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), regulate both cancer and immune cells and thus can potentially improve antitumor responses. Here, we review recent advances in research of tumor-infiltrating Tregs, with a focus on the functional roles of immune checkpoint and inhibitory Tregs receptors and the regulatory mechanisms of ncRNAs in Treg plasticity and functionality.

LncRNA and its role in gastric cancer immunotherapy

Gastric cancer (GC) is a potential dominant disease in tumor immunotherapy checkpoint inhibitors, and adoptive cell therapy have brought great hope to GC patients. However, only some patients with GC can benefit from immunotherapy, and some patients develop drug resistance. More and more studies have shown that long non-coding RNAs (lncRNAs) may be important in GC immunotherapy's prognosis and drug resistance. Here, we summarize the differential expression of lncRNAs in GC and their impact on the curative effect of GC immunotherapy, discuss potential mechanisms of activity in GC immunotherapy resistance regulated by lncRNAs. This paper reviews the differential expression of lncRNA in GC and its effect on immunotherapy efficacy in GC. In terms of genomic stability, inhibitory immune checkpoint molecular expression, the cross-talk between lncRNA and immune-related characteristics of GC was summarized, including tumor mutation burden (TMB), microsatellite instability (MSI), and Programmed death 1 (PD-1). At the same time, this paper reviewed the mechanism of tumor-induced antigen presentation and upregulation of immunosuppressive factors, as well as the association between Fas system and lncRNA, immune microenvironment (TIME) and lncRNA, and summarized the functional role of lncRNA in tumor immune evasion and immunotherapy resistance.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma. It is particularly important to accurately judge the prognosis of patients. Since most tumor prediction models depend on the specific expression level of related genes, a better model therefore needs to be constructed. To provide an immune-related lncRNA (irlncRNAs) tumor prognosis model that is independent of the specific gene expression levels, we first downloaded and sorted out the data on ccRCC in the TCGA database and screened irlncRNAs using co-expression analysis and then obtained the differently expressed irlncRNA (DEirlncRNA) pairs by means of univariate analysis. In addition, we modified LASSO penalized regression. Subsequently, the ROC curve was drawn, and we compared the area under the curve, calculated the Akaike information standard value of the 5-year receiver operating characteristic curve, and determined the cut-off point to establish the best model to distinguish the high- or low-disease-risk group of ccRCC. Subsequently, we reassessed the model from the perspectives of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. A total of 17 DEirlncRNAs pairs (AL031710.1|AC104984.5, AC020907.4|AC127-24.4,AC091185.1|AC005104.1, AL513218.1|AC079015.1, AC104564.3|HOXB-AS3, AC003070.1|LINC01355, SEMA6A-AS1|CR936218.1, AL513327.1|AS005785.1, AC084876.1|AC009704.2, IGFL2-AS1|PRDM16-DT, AC011462.4|MMP25-AS1, AL662844.3I|TGB2-AS1, ARHGAP27P1|AC116914.2, AC093788.1|AC007098.1, MCF2L-AS1|AC093001.1, SMIM25|AC008870.2, and AC027796.4|LINC00893) were identified, all of which were included in the Cox regression model. Using the cut-off point, we can better distinguish patients according to different factors, such as survival status, invasive clinic-pathological features, tumor immune infiltration, whether they are sensitive to chemotherapy or not, and expression of immunosuppressive biomarkers. We constructed the irlncRNA model by means of pairing, which can better eliminate the dependence on the expression level of the target genes. In other words, the signature established by pairing irlncRNA regardless of expression levels showed promising clinical prediction value.

A Novel Angiogenesis-Related Gene Signature to Predict Biochemical Recurrence of Patients with Prostate Cancer following Radical Therapy

Background

Prostate cancer (PCa) is one of the most common malignancies in males; we aim to establish a novel angiogenesis-related gene signature for biochemical recurrence (BCR) prediction in PCa patients following radical therapy.

Methods

Gene expression profiles and corresponding clinicopathological data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We quantified the levels of various cancer hallmarks and identified angiogenesis as the primary risk factor for BCR. Then machine learning methods combined with Cox regression analysis were used to screen prognostic genes and construct an angiogenesis-related gene signature, which was further verified in external cohorts. Furthermore, estimation of immune cell abundance and prediction of drug responses were also conducted to detect potential mechanisms.

Results

Angiogenesis was regarded as the leading risk factor for BCR in PCa patients (HR = 1.58, 95% CI: 1.38–1.81), and a novel prognostic signature based on three genes (NRP1, JAG2, and VCAN) was developed in the training cohort and successfully validated in another three independent cohorts. In all datasets, this signature was identified as a prognostic factor with promising and robust predictive values. Moreover, it also predicted higher infiltration of regulatory T cells and M2-polarized macrophages and increased drug sensitivity of angiogenesis inhibitors in high-risk patients.

Conclusions

The angiogenesis-related three-gene signature may serve as an independent prognostic factor for BCR, which would contribute to risk stratification and personalized management of PCa patients after radical therapy in clinical practice.

Exploration of Prognostic Immune-Related Genes and lncRNAs Biomarkers in Kidney Renal Clear Cell Carcinoma and Its Crosstalk with Acute Kidney Injury

Kidney renal clear cell carcinoma (KIRC) has a poor prognosis and a high death rate globally. Cancer prognosis is strongly linked to immune-related genes (IRGs), according to numerous research. We utilized KIRC RNA-seq data from the TCGA database to build a prognostic model incorporating seven immune-related (IR) lncRNAs, and we constructed the model using LASSO regression. Additionally, we calculated a risk score for each patient using a prognostic model that divided patients into high-risk and low-risk groups. The ESTIMATE and CIBERSORT methodologies were then used to analyze the differences in the tumor microenvironment of the two groups of patients. Finally, we predicted three small molecule drugs that may have potential therapeutic effects for high-risk patients. We combined the acute kidney injury dataset to obtain differential genes that may serve standard biological functions with two risk groups. Our study shows that the model we constructed for IR-lncRNAs has reliable predictive efficacy for patients with KIRC.

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.