Tumor-Infiltrating Immune Cell Signature Predicts the Prognosis and Chemosensitivity of Patients With Pancreatic Ductal Adenocarcinoma

The role of specialized pro-resolving mediators (SPMs) in inflammatory arthritis: A therapeutic strategy

Rheumatoid arthritis (RA) is classified as a persistent inflammatory autoimmune disorder leading to the subsequent erosion of articular cartilage and bone tissue originating from the synovium. The fundamental objective of therapeutic interventions in RA has been the suppression of inflammation. Nevertheless, conventional medicines that lack target specificity may exhibit unpredictable effects on cell metabolism. In recent times, there has been evidence suggesting that specialized pro-resolving mediators (SPMs), which are lipid metabolites, have a role in facilitating the resolution of inflammation and the reestablishment of tissue homeostasis. SPMs are synthesized by immune cells through the enzymatic conversion of omega-3 fatty acids. In the context of RA, there is a possibility of dysregulation in the production of these SPMs. In this review, we delve into the present comprehension of the endogenous functions of SPMs in RA as lipids that exhibit pro-resolutive, protective, and immunoresolvent properties.

Developing and validating the model of tumor-infiltrating immune cell to predict survival in patients receiving radiation therapy for head and neck squamous cell carcinoma

Background

Radiotherapy (RT) is a mainstay of head and neck squamous cell carcinoma (HNSCC) treatment. Due to the influence of RT on tumor cells and immune/stromal cells in microenvironment, some studies suggest that immunologic landscape could shape treatment response. To better predict the survival based on genomic data, we developed a prognostic model using tumor-infiltrating immune cell (TIIC) signature to predict survival in patients undergoing RT for HNSCC.

Methods

Gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Data from HNSCC patients undergoing RT were extracted for analysis. TIICs prevalence in HNSCC patients was quantified by gene set variation analysis (GSVA) algorithm. TIICs and post-RT survival were analyzed using univariate Cox regression analysis and used to construct and validate a tumor-infiltrating cells score (TICS).

Results

Five of 26 immune cells were significantly associated with HNSCC prognosis in the training cohort (all P<0.05). Kaplan-Meier (KM) survival curves showed that patients in the high TICS group had better survival outcomes (log-rank test, P<0.05). Univariate analyses demonstrated that the TICS had independent prognostic predictive ability for RT outcomes (P<0.05). Patients with high TICS scores showed significantly higher expression of immune-related genes. Functional pathway analyses further showed that the TICS was significantly related to immune-related biological process. Stratified analyses supported integrating TICS and tumor mutation burden (TMB) into individualized treatment planning, as an adjunct to classification by clinical stage and human papillomavirus (HPV) infection.

Conclusions

The TICS model supports a personalized medicine approach to RT for HNSCC. Increased prevalence of TIIC within the tumor microenvironment (TME) confers a better prognosis for patients undergoing treatment for HNSCC.

The Role of Chemokines in Orchestrating the Immune Response to Pancreatic Ductal Adenocarcinoma

Chemokines are small molecules that function as chemotactic factors which regulate the migration, infiltration, and accumulation of immune cells. Here, we comprehensively assess the structural and functional role of chemokines, examine the effects of chemokines that are present in the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME), specifically those produced by cancer cells and stromal components, and evaluate their impact on immune cell trafficking, both in promoting and suppressing anti-tumor responses. We further explore the impact of chemokines on patient outcomes in PDAC and their role in the context of immunotherapy treatments, and review clinical trials that have targeted chemokine receptors and ligands in the treatment of PDAC. Lastly, we highlight potential strategies that can be utilized to harness chemokines in order to increase cytotoxic immune cell infiltration and the anti-tumor effects of immunotherapy.

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

Pro-resolving and anti-inflammatory effects of resolvins and protectins in rheumatoid arthritis

Rheumatoid arthritis (RA) is typified by persistent joint inflammation, which leads to the deterioration of bone and cartilage and a reduction in overall quality of life. The global prevalence of pain as a primary symptom in RA is influenced by the interplay between inflammation and its resolution. The identification of a family of lipid mediators known as specialized pro-resolving mediators (SPM)s has contributed to the progress of our comprehension of inflammatory conditions. SPMs have been observed to trigger the process of inflammation resolution, thereby reinstating the homeostasis of the inflammatory response. Autacoids are synthesized through the stereo-selective transformation of essential fatty acids, resulting in molecules dynamically modulated during inflammation and possessing strong immunoregulatory properties. This review delves into the available evidence that supports the involvement of certain SPM as protective lipids, biomarkers with potential, and therapeutic targets in the context of RA.

Liver metastases across cancer types sharing tumor environment immunotolerance can impede immune response therapy and immune monitoring

BACKGROUND

Hepatic immune tolerance might contribute to the development of therapeutic resistance to immunotherapy. However, addressing this issue is challenging since the efficacy of immunotherapy in the context of liver metastasis (LM) remains poorly studied. Here, we aimed to establish an LM common immune feature (LMCIF) score to quantify the characteristics of LM immunotolerance across cancer types for assisting clinical disease management.

METHODS

Large-scale clinical data were collected to identify the prognosis of LM. Multi-omics datasets of metastatic cancers with LM special immune-related pathways (LMSIPs) from the Molecular Signatures Database (MSigDB)were used to obtain an LMCIF cluster. Based on differential expression genes (DEGs), a novel LMCIF score for the LMCIF cluster was constructed. In addition, multi-omics, and immunohistochemistry (IHC) data from the public and in-house cohorts were used to explore the features of LM, and LMCIF score.

RESULTS

Patients with LM had a worse prognosis and significantly lower infiltration of immune cells than patients with metastasis to other organs when analyzed with combined clinical and RNA sequencing data. After extracting the LMCIF cluster from 373 samples by utilizing 29 LMSIPs and validating them in a microarray cohort, an LMCIF score was established to confirm the role of the immunosuppressive environment as a contributor to the poor prognosis of LM across cancer types. Moreover, this LMCIF score could be used to predict the immune response of cancer patients undergoing immunotherapy. Finally, we identified that the majority of the 31 LMCIF genes exhibited a negative correlation with TME cells in LM patients, one of them, KRT19, which possessed the strongest positive correlation with LMCIF score, was confirmed to have an immunosuppressive effect through IHC analysis.

CONCLUSIONS

Our results suggest that LM across cancer types share similar immunological profiles that induce immunotolerance and escape from immune monitoring. The novel LMCIF score represents a common liver metastasis immune cluster for predicting immunotherapy response, the results of which might benefit clinical disease management.

Platelet-derived microvesicles (PMVs) in cancer progression and clinical applications

Platelet-derived microvesicles (PMVs), the microvesicles with the highest concentration in the bloodstream, play a key role in the regulation of hemostasis, inflammation, and angiogenesis. PMVs have recently been identified as key factors in the link between platelets and cancer. PMVs bind to both cancer cells and nontransformed cells in the microenvironment of the tumor, and then transfer platelet-derived contents to the target cell. These contents have the potential to either stimulate or modulate the target cell's response. PMVs are encased in a lipid bilayer that contains surface proteins and lipids as well as components found inside the PMV. Each of these components participates in known and potential PMV roles in cancer. The complicated roles played by PMVs in the onset, development, and progression of cancer and cancer-related comorbidities are summarized in this study.

Association between disease-modifying antirheumatic drugs and bone turnover biomarkers

Rheumatoid arthritis (RA) has been linked to an increased risk of osteoporosis as well as fractures. Patients diagnosed with RA had a 25% increased risk of osteoporotic fracture, according to a recent population-based cohort study that compared them to people without RA. Several studies have found a correlation between osteoporosis and the presence of pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1, and 6. These cytokines play a crucial part in the process of bone resorption by boosting osteoclast activation and encouraging osteoclast differentiation. Based on the correlation between RA, osteoporosis, and inflammation, it is possible that systemic immunosuppression with disease-modifying antirheumatic drugs (DMARDs) can help individuals with RA have a lower chance of developing osteoporosis and osteoporotic fractures. There is little information on how different DMARDs, biologic or non-biologic, affect RA patients' bone metabolism. In this study, we present an overview of the influence that targeted therapies, such as biologics, non-biologics, and small molecule inhibitors, have on bone homeostasis in RA patients.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

Ferroptosis is a recently established type of iron-dependent programmed cell death. Growing studies have focused on the function of ferroptosis in cancers, including lung adenocarcinoma (LUAD). However, the factors involved in the regulation of ferroptosis-related genes are not fully understood. In this study, we collected data from lung adenocarcinoma datasets of the Cancer Genome Atlas (TCGA-LUAD). The expression profiles of 60 ferroptosis-related genes were screened, and two differentially expressed ferroptosis subtypes were identified. We found the two ferroptosis subtypes can predict clinical outcomes and therapeutic responses in LUAD patients. Furthermore, key long non-coding RNAs (lncRNAs) were screened by single factor Cox and least absolute shrinkage and selection operator (LASSO) based on which co-expressed with the 60 ferroptosis-related genes. We then established a risk score model which included 13 LUAD ferroptosis-related lncRNAs with a multi-factor Cox regression. The risk score model showed a good performance in evaluating the outcome of LUAD. What's more, we divided TCGA-LUAD tumor samples into two groups with high- and low-risk scores and further explored the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration among different LUAD tumor risk score groups and evaluate the predictive ability of risk score for immunotherapy benefit. Our findings provide good support for immunotherapy in LUAD in the future.

CRISPR-Cas9-mediated gene therapy in lung cancer

As the largest cause of cancer-related deaths worldwide, pulmonary cancer is the most common form of the disease. Several genetic, epigenetic, and environmental factors come into play during the multi-step mechanism of tumorigenesis. The heterogeneity that makes discovering successful therapeutics for pulmonary cancer problematic is significantly influenced by the epigenetic landscape, including DNA methylation, chromatin architecture, histone modifications, and noncoding RNA control. Clinical activity of epigenetic-targeted medicines has been reported in hematological tumors, and these compounds may also have therapeutic effects in solid tumors. Over the course of the past few years, some researchers have successfully modified the expression of genes in cells using the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) technique. The utilization of this technology allows for the induction of site-specific mutagenesis, epigenetic alterations, and the regulation of gene expression. This study will present an overview of the primary epigenetic alterations seen in pulmonary cancer, as well as a summary of therapeutic implications for targeting epigenetics in the management of pulmonary cancer, with a particular emphasis on the technique known as CRISPR/Cas9.

The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma

Introduction

Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored.

Methods

We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Finally, we explored the prognostic and immunotherapeutic value of these CAF-related subtypes for PAAD.

Results

CAFs were divided into five new subclusters (CAF-C0, CAF-C1, CAF-C2, CAF-C3, and CAF-C4) based on their marker genes. The five CAF subclusters exhibited distinct signaling patterns, immune status, metabolism features, and enrichment pathways and validated in the pan-cancer datasets. In addition, we found that both CAF-C2 and CAF-C4 subgroups were negatively correlated with prognosis. With their top genes of each subclusters, the sub-CAF2 had significantly relations to immunotherapy response in the patients with pan-cancer and immunotherapy.

Discussion

We explored the heterogeneity of five subclusters based on CAF in signaling patterns, immune status, metabolism features, enrichment pathways, and prognosis for PAAD.

Understanding the role of Cripto-1 in cancer progression and therapeutic strategies

During the initial stages of gastrulation during embryonic differentiation and wound healing, Cripto-1 is a critical protein for human growth. The epithelial adhesion molecules' downregulation, the mesenchymal overexpression, and mobile proteins are important mechanisms by which Cripto-1 initiates epithelial to mesenchymal transition (EMT). As a result, the function of Cripto-1 for inducing EMT to increase cell migration is advantageous during embryogenesis; however, it is deleterious during the formation, growth, and malignant tumor metastasis. The majority of malignancies are reported to have elevated levels of Cripto-1. Cripto-1 can modify cancerous cells through its function in EMT, which enables these cells to migrate via the extracellular matrix, bloodstream, and lymphatic vessels, on their way for metastasizing to other organs. The goal of this review is to explain what role Cripto-1 plays in common cancers and to summarize how therapeutic strategies are used to interfere with this molecule to target cancers.

Cytokines chattering in pancreatic ductal adenocarcinoma tumor microenvironment

Pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME) consists of multiple cell types interspersed by dense fibrous stroma. These cells communicate through low molecular weight signaling molecules called cytokines. The cytokines, through their receptors, facilitate PDAC initiation, progression, metastasis, and distant colonization of malignant cells. These signaling mediators secreted from tumor-associated macrophages, and cancer-associated fibroblasts in conjunction with oncogenic Kras mutation initiate acinar to ductal metaplasia (ADM), resulting in the appearance of early preneoplastic lesions. Further, M1- and M2-polarized macrophages provide proinflammatory conditions and promote deposition of extracellular matrix, whereas myofibroblasts and T-lymphocytes, such as Th17 and T-regulatory cells, create a fibroinflammatory and immunosuppressive environment with a significantly reduced cytotoxic T-cell population. During PDAC progression, cytokines regulate the expression of various oncogenic regulators such as NFκB, c-myc, growth factor receptors, and mucins resulting in the formation of high-grade PanIN lesions, epithelial to mesenchymal transition, invasion, and extravasation of malignant cells, and metastasis. During metastasis, PDAC cells colonize at the premetastatic niche created in the liver, and lung, an organotropic function primarily executed by cytokines in circulation or loaded in the exosomes from the primary tumor cells. The indispensable contribution of these cytokines at every stage of PDAC tumorigenesis makes them exciting candidates in combination with immune-, chemo- and targeted radiation therapy.

The immune escape signature predicts the prognosis and immunotherapy sensitivity for pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies worldwide. Immune escape is considered to be a reason for immunotherapy failure in PDAC. In this study, we explored the correlation between immune escape-related genes and the prognosis of PDAC patients.

Methods

1163 PDAC patients from four public databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Array-express, and Gene Expression Omnibus (GEO), were included in our study. Cox regression analysis was used to identify the 182 immune genes which were significantly associated with overall survival (OS). And then we established an immune escape-related gene prognosis index (IEGPI) score using several datasets as the training cohort and validated it using the validation cohort. Kaplan-Meier (KM) and Cox regression analysis were used to detect the relationship of IEGPI score with OS. We further explored the relationship between the IEGPI and immune indexes. And the prediction value of response for immunotherapy in Tumor Immune Dysfunction and Exclusion (TIDE) dataset.

Results

We establish an IEGPI score based on 27 immune escape genes which were significantly related to the prognosis of OS in PDAC patients. Patients in the high-IEGPI group had a significantly worse overall survival rate compared with that in the low-IEGPI groups by KM curves and cox-regression. 5 of the 32 cancer types in TCGA could be significantly distinguished in survival rates through the low- and high-IEGPI groups. Moreover, the correlation between the IEGPI score was negatively correlated with an immune score in several datasets. And higher IEGPI better recurrence-free survival (RFS) and OS in the patients after patients were treated with both PD-1 and CTLA4 in the public datasets (P<0.05). Intriguingly, by using RT-PCR, we verified that the gene of PTPN2, CEP55, and JAK2 were all higher in the BxPC-3 and PANC-1 than HPDE5 cells. Lastly, we found that the IEGPI score was higher in K-rasLSL.G12D/+, p53LSL.R172H/+, Pdx1Cre (KPC) mice model with anti-PD-L1 than that without anti-PD-L1.

Conclusion

Using the immune escape-related genes, our study established and validated an IEGPI score in PDAC patients from the public dataset. IEGPI score has the potential to serve as a prognostic marker and as a tool for selecting tumor patients suitable for immunotherapy in clinical practice.

Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma

Background

Patients with lung adenocarcinoma (LUAD) exhibit significant heterogeneity in therapeutic responses and overall survival (OS). In recent years, accumulating research has uncovered the critical roles of hypoxia in a variety of solid tumors, but its role in LUAD is not currently fully elucidated. This study aims to discover novel insights into the mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers by exploring the potential association between hypoxia and LUAD.

Methods

Four machine learning approaches were implemented to screen out potential hypoxia-related genes for the prognosis of LUAD based on gene expression profile of LUAD samples obtained from The Cancer Genome Atlas (TCGA), then validated by six cohorts of validation datasets. The risk score derived from the hypoxia-related genes was proven to be an independent factor by using the univariate and multivariate Cox regression analyses and Kaplan-Meier survival analyses. Hypoxia-related mechanisms based on tumor mutational burden (TMB), the immune activity, and therapeutic value were also performed to adequately dig deeper into the clinical value of hypoxia-related genes. Finally, the expression level of hypoxia genes was validated at protein level and clinical samples from LUAD patients at transcript levels.

Results

All patients in TCGA and GEO-LUAD group were distinctly stratified into low- and high-risk groups based on the risk score. Survival analyses demonstrated that our risk score could serve as a powerful and independent risk factor for OS, and the nomogram also exhibited high accuracy. LUAD patients in high-risk group presented worse OS, lower TMB, and lower immune activity. We found that the model is highly sensitive to immune features. Moreover, we revealed that the hypoxia-related genes had potential therapeutic value for LUAD patients based on the drug sensitivity and chemotherapeutic response prediction. The protein and gene expression levels of 10 selected hypoxia gene also showed significant difference between LUAD tumors tissues and normal tissues. The validation experiment showed that the gene transcript levels of most of their genes were consistent with the levels of their translated proteins.

Conclusions

Our study might contribute to the optimization of risk stratification for survival and personalized management of LUAD patients by using the hypoxia genes, which will provide a valuable resource that will guide both mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers.

Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet.

Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively.

Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4⁺/CD8⁺ T cell, activated dendritic cell, and effector memory CD8⁺ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC.

Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.

Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.

Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.

Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

A quantitative score of immune cell infiltration predicts the prognosis in pancreatic ductal adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) is characterized by an extensive and dense fibrous stroma, which plays an active role in tumor growth and metastasis. Despite the growing importance of the tumor microenvironment in PDAC prognosis, the immune cell infiltration landscape of PDAC has not been elucidated. In this study, we applied a credible computational algorithm to comprehensively estimate the immune cell infiltration (ICI) patterns of 876 PDAC patients. Two ICI phenotypes were identified, and a ICIscore was constructed using ssGSEA algorithm. The ICIscore could significantly predict the prognosis and chemotherapy benefit of PDAC patients in both the discovery and the five validation cohorts. Multivariate cox analysis also identified the independent predictive role of the ICIscore in PDAC prognosis. A high ICIscore subtype was characterized by immune-active signaling pathways and anti-tumor immunity while a low ICIscore subtype was associated with tumor progressive signaling pathways. Four immunotherapy cohorts further supported the use of the ICIscore as a prognostic biomarker for patients receiving immune checkpoint inhibitors in other cancer types. The ICIscore reveals a close relationship between the ICI environment and prognosis and may provide new treatment strategies for PDAC patients.