An individualized immune signature of pretreatment biopsies predicts pathological complete response to neoadjuvant chemoradiotherapy and outcomes in patients with esophageal squamous cell carcinoma

Prognostic analysis of esophageal cancer patients after neoadjuvant therapy

Background

Neoadjuvant therapy is widely used for esophageal cancer (EC), but optimal treatment regimens and predictive factors for outcomes remain unclear. This study retrospectively analyzed data from EC patients who underwent neoadjuvant therapy.

Methods

The chi-square test or Fisher's exact test was utilized to examine differences in general clinicopathological data between treatment benefit groups. Survival analyses were conducted using Kaplan-Meier methods. Cox univariate and multivariate regression analyses were employed to identify independent risk factors affecting overall survival (OS) in EC patients receiving different treatment modalities.

Results

The study included 175 EC patients who underwent neoadjuvant therapy. Analysis of clinical benefit differences revealed that patients aged < 65 years (P = 0.028) and those with esophageal squamous cell carcinoma (ESCC) (P = 0.027) were more likely to achieve a complete response, while N1 patients more frequently attained an objective response (P < 0.001). OS analysis indicated that patients who did not receive immunotherapy exhibited better survival outcomes compared to those who did (P = 0.002). Patients with pretreatment N3 status demonstrated poorer survival compared to those with N0 (P = 0.004), N1 (P = 0.003), and N2 (P = 0.003) status. Among post-neoadjuvant EC patients who did not receive immunotherapy, those with primary tumors located in the middle esophagus (hazard ratio [HR], 0.181; 95% Confidence interval (CI) = 0.044-0.739; P = 0.017) and lower esophagus (HR, 0.163; 95%CI = 0.032-0.821; P = 0.028) demonstrated a better prognosis compared to patients with tumors in the upper esophagus. Notably, EC patients who did not receive immunotherapy after neoadjuvant therapy and underwent 3-6 cycles of therapy exhibited a poorer prognosis compared to those who received 1-2 cycles (HR, 2.731; 95%CI = 1.187-6.284; P = 0.018).

Conclusions

In conclusion, this study found that immunotherapy did not play a decisive role in neoadjuvant EC therapy. Instead, 1-2 cycles of chemotherapy or chemoradiotherapy were associated with a more favorable prognosis for these patients.

Advancements in immunotherapy for advanced esophageal squamous cell carcinoma: a comprehensive review of current strategies and future directions

INTRODUCTION

Esophageal cancer (EC), particularly esophageal squamous cell carcinoma (ESCC), is characterized by high incidence and poor prognosis worldwide, necessitating novel therapeutic approaches like immunotherapy. This review explores the impact of immune checkpoint inhibitors (ICIs) on ESCC, especially focusing on PD-1/PD-L1 and CTLA-4 inhibitors. Our literature search, conducted across databases including PubMed, Web of Science, and EMBASE, from January 2010 to December 2023, aimed at identifying advancements, challenges, and future directions in the use of immunotherapy for ESCC.

AREAS COVERED

We provide a detailed analysis of clinical trials evaluating the efficacy of ICIs as monotherapy and in combination with chemotherapy, radiotherapy, and targeted therapy for locally advanced ESCC. Our findings highlight the significant survival benefits offered by ICIs, albeit with varying efficacy across patient populations, emphasizing the need for precise biomarkers to tailor treatment strategies.

EXPERT OPINION

The integration of immunotherapy into the ESCC treatment paradigm represents a significant shift, improving survival outcomes. Future research should focus on optimizing combination therapies and novel immunotherapeutic agents, incorporating genetic and tumor microenvironment analyses to enhance patient selection and treatment efficacy.

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Background

In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective

To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods

Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.

Results

RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.

Conclusion

We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma

PURPOSE

A pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in up to 40% of the patients with esophageal squamous cell carcinoma (ESCC). No nomogram has been constructed for the prediction of pCR for patients whose primary chemotherapy was a taxane-based regimen. The aim is to identify characteristics associated with a pCR through analyzing multiple pre- and post-nCRT variables and to develop a nomogram for the prediction of pCR for these patients by integrating clinicopathological characteristics and hematological biomarkers.

MATERIALS AND METHODS

We analyzed 293 patients with ESCC who underwent nCRT followed by esophagectomy. Clinicopathological factors, hematological parameters before nCRT, and hematotoxicity during nCRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and evaluated for both discrimination and calibration.

RESULTS

After surgery, 37.88% of the study patients achieved pCR. Six variables were included in the nomogram: sex, cN stage, chemotherapy regimen, duration of nCRT, pre-nCRT neutrophil-to-lymphocyte ratio (NLR), and pre-nCRT platelet-to-lymphocyte ratio (PLR). The nomogram indicated good accuracy and consistency in predicting pCR, with a C-index of 0.743 (95% confidence interval: 0.686, 0.800) and a p value of 0.600 (>0.05) in the Hosmer-Lemeshow goodness-of-fit test.

CONCLUSIONS

Female, earlier cN stage, duration of nCRT (< 62 days), chemotherapy regimen of taxane plus platinum, pre-nCRT NLR (≥2.199), and pre-nCRT PLR (≥99.302) were significantly associated with a higher pCR in ESCC patients whose primary chemotherapy was a taxane-based regimen for nCRT. A nomogram was developed and internally validated, showing good accuracy and consistency.

Clinical significance, molecular characterization, and immune microenvironment analysis of coagulation-related genes in clear cell renal cell carcinoma

BACKGROUND

Numerous studies have revealed a tight connection between tumor development and the coagulation system. However, the effects of coagulation on the prognosis and tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remain poorly understood.

METHODS

We employed the consensus clustering method to characterize distinct molecular subtypes associated with coagulation patterns. Subsequently, we examined variations in the overall survival (OS), genomic profiles, and TME characteristics between these subtypes. To develop a prognostic coagulation-related risk score (CRRS) model, we utilized the least absolute shrinkage and selection operator Cox regression and stepwise multivariate Cox regression analyses. We also created a nomogram to aid in the clinical application of the risk score, evaluating the relationships between the CRRS and the immune microenvironment, responsiveness to immunotherapy, and targeted treatment. The clinical significance of PLAUR and its biological function in ccRCC were also further analyzed.

RESULTS

There were significant differences in clinical features, prognostic stratification, genomic variation, and TME characteristics between the two coagulation-related subtypes. We established and validated a CRRS using six coagulation-related genes that can be employed as an effective indicator of risk stratification and prognosis estimation for ccRCC patients. Significant variations in survival outcomes were observed between the high- and low-risk groups. The nomogram was proficient in predicting the 1-, 3-, and 5-year OS. Additionally, the CRRS emerged as a novel tool for evaluating the clinical effectiveness of immunotherapy and targeted treatments in ccRCC. Moreover, we confirmed upregulated PLAUR expression in ccRCC samples that was significantly correlated with poor patient prognosis. PLAUR knockdown notably inhibited ccRCC cell proliferation and migration.

CONCLUSION

Our data suggested that CRRS may be employed as a reliable predictive biomarker that can provide therapeutic benefits for immunotherapy and targeted therapy in ccRCC.

A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma

Background

There is a heterogenous clinical response following chemoradiotherapy (CRT) in esophageal squamous cell carcinoma (ESCC). Therefore, we aimed to study signaling pathway genes that affect CRT sensitivity and prognosis.

Methods

Gene expression analyses were performed in the GEO and TCGA datasets. A immunohistochemistry (IHC) analysis was performed in pretreatment biopsies.

Results

MMP13 was found to be highly expressed in the "Pathologic Complete Response (pCR)" and "Complete Remission (CR)" and "Alive" groups. Th17 cells and MMP9/13 showed a negative correlation in immune infiltration analysis. In GSEA analysis, IL-4 and IL-13 signaling pathways were highly enriched in patients exhibiting high MMP expression in pCR and CR groups. IHC results suggested higher MMP13 & IL-4 and lower IL-17A & RORC expression in the CR group compared to the 0.70, and the model could well distinguish high-risk and low-risk subgroups.

Conclusion

The above results may provide guidance for developing novel treatment and prognostic strategies in ESCC patients.

Molecular phenotypic linkage between N6-methyladenosine methylation and tumor immune microenvironment in hepatocellular carcinoma

PURPOSE

The crucial role of N6-methyladenosine (m6A) methylation in anti-tumor immunity and immunotherapy has been broadly depicted. However, the molecular phenotypic linkages between m6A modification pattern and immunological ecosystem are expected to be disentangled in hepatocellular carcinoma (HCC), for immunotherapeutic unresponsiveness circumvention and combination with promising drug agents.

METHODS

Modification patterns of m6A methylation were qualitatively dissected according to the large-scale HCC samples profiling. We then determined the immune phenotypic linkages by systematically evaluating their tumor microenvironment composition, immune/stromal-relevant signature, immune checkpoints correlation, and prognostic value. Individual quantification of m6A methylation pattern was achieved by m6Ascore construction, intensified by longitudinal single-cell analysis of immunotherapy cohort and validated by the transcriptomic profiles of our in-hospital GDPH-HCC cohort. Candidate therapeutic agents were also screened out.

RESULTS

Three distinct m6A methylation patterns were determined in high accordance with inflamed-, excluded-, and desert-immunophenotype. To be precise, Immune-inflamed high-m6Ascore group was characterized by activated immunity with favorable prognosis. Stromal activation and absence of immune cell infiltration were observed in low-m6Ascore phenotype, linked to impaired outcome. Patients with low-m6Ascore demonstrated diminished responses and clinical benefits for cohorts receiving immunotherapy. The above credible linkage between m6A methylation pattern and tumor immune microenvironment was robustly validated in our GDPH-HCC cohort. Single-cell dynamic change of m6A methylation level in exhausted CD8 T cell and fibroblast was depicted in immunotherapy cohort fore and art. Derived from m6A methylation pattern, seven potential frontline drug agents were recognized as promising choice for high-m6Ascore patients.

CONCLUSION

Our work bridged the credible linkage between epigenetics and anti-tumor immunity in HCC, unraveling m6A modification pattern as immunological indicator and predictor for immunotherapy. Individualized m6Ascore facilitated strategic choices to maximize therapy-responsive possibility.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Src heterodimerically activates Lyn or Fyn to serve as targets for the diagnosis and treatment of esophageal squamous cell carcinoma

Although Src is one of the oldest and most investigated oncoproteins, its function in tumor malignancy remains to be defined further. In this study, we demonstrated that the inhibition of Src activity by ponatinib effectively suppressed several malignant phenotypes of esophageal squamous cell carcinoma (ESCC) both in vitro and in vivo, whereas it did not produce growth-inhibitory effects on normal esophageal epithelial cells (NEECs). Importantly, we combined phosphoproteomics and several cellular and molecular biologic strategies to identify that Src interacted with the members of Src-family kinases (SFKs), such as Fyn or Lyn, to form heterodimers. Src interactions with Fyn and Lyn phosphorylated the tyrosine sites in SH2 (Fyn Tyr¹⁸⁵ or Lyn Tyr¹⁸³) and kinase domains (Fyn Tyr⁴²⁰ or Lyn Tyr³⁹⁷), which critically contributed to ESCC development. By contrast, Src could not form heterodimers with Fyn or Lyn in NEECs. We used RNA sequencing to comprehensively demonstrate that the inhibition of Src activity effectively blocked several critical tumor-promoting pathways, such as JAK/STAT, mTOR, stemness-related, and metabolism-related pathways. Results of the real-time polymerase chain reaction (RT-PCR) assay confirmed that Lyn and Fyn were critical effectors for the Src-mediated expression of tumor growth or metastasis-related molecules. Furthermore, results of the clinical ESCC samples showed that the hyperactivation of pSrc Tyr⁴¹⁹, Fyn Tyr¹⁸⁵ or Tyr⁴²⁰, and Lyn Tyr¹⁸³ or Tyr³⁹⁷ could be biomarkers of ESCC prognosis. This study illustrates that Src/Fyn and Src/Lyn heterodimers serve as targets for the treatment of ESCC.

A meta-learning approach to improving radiation response prediction in cancers

PURPOSE

Predicting the efficacy of radiotherapy in individual patients has drawn widespread attention, but the limited sample size remains a bottleneck for utilizing high-dimensional multi-omics data to guide personalized radiotherapy. We hypothesize the recently developed meta-learning framework could address this limitation.

METHODS AND MATERIALS

By combining gene expression, DNA methylation, and clinical data of 806 patients who had received radiotherapy from The Cancer Genome Atlas (TCGA), we applied the Model-Agnostic Meta-Learning (MAML) framework to tasks consisting of pan-cancer data, to obtain the best initial parameters of a neural network for a specific cancer with smaller number of samples. The performance of meta-learning framework was compared with four traditional machine learning methods based on two training schemes, and tested on Cancer Cell Line Encyclopedia (CCLE) and Chinese Glioma Genome Atlas (CGGA) datasets. Moreover, biological significance of the models was investigated by survival analysis and feature interpretation.

RESULTS

The mean AUC (Area under the ROC Curve) [95% confidence interval] of our models across nine cancer types was 0.702 [0.691-0.713], which improved by 0.166 on average over other the four machine learning methods on two training schemes. Our models performed significantly better (p < 0.05) in seven cancer types and performed comparable to the other predictors in the rest of two cancer types. The more pan-cancer samples were used to transfer meta-knowledge, the greater the performance improved (p < 0.05). The predicted response scores that our models generated were negatively correlated with cell radiosensitivity index in four cancer types (p < 0.05), while not statistically significant in the other three cancer types. Moreover, the predicted response scores were shown to be prognostic factors in seven cancer types and eight potential radiosensitivity-related genes were identified.

CONCLUSIONS

For the first time, we established the meta-learning approach to improving individual radiation response prediction by transferring common knowledge from pan-cancer data with MAML framework. The results demonstrated the superiority, generalizability, and biological significance of our approach.

A tumor immune microenvironment-related integrated signature can predict the pathological response and prognosis of esophageal squamous cell carcinoma following neoadjuvant chemoradiotherapy: A multicenter study in China

BACKGROUND

Currently, there are insufficient indicators for the reliable assessment of treatment response following neoadjuvant chemoradiotherapy (nCRT) in patients with esophageal squamous cell carcinoma (ESCC). Considering the essential role of protein-coding and non-coding RNAs in gene regulation and cellular processes, we systematically explored the molecular features and clinical significance of mRNA and lncRNA in 249 pretreatment biopsies from four hospitals in three districts with a high incidence of ESCC patients in China.

METHODS

During the discovery phrase, 13 differentially expressed genes were identified and validated between samples with a complete pathological response (pCR) and those with an incomplete pathological response (<pCR). Subsequently, we constructed a predictive mRNA and lncRNA signature (SERPINE1, LINC00592, and PRKAG2-AS1) using Fisher's linear discriminant analysis (FLDA) with stepwise variant-selection, followed by validation of its predictive ability in both internal and external cohorts.

RESULTS

Our signature showed great value in predicting the response to nCRT among ESCC samples and acted as an independent predictive indicator, in addition to demonstrating great potential in estimating patient prognosis. Interestingly, we found that patients with a high signature score had lower PD-L1 and IDO1 expression levels but higher CD8⁺ T cells infiltration, suggesting that PD-L1 and IDO1 are negatively correlated with a high signature score and further associated with pCR and a better prognosis.

CONCLUSION

The present study identified a promising three-gene-based predictive signature that has powerful clinical implications for the identification of pCR and a good prognosis among patients with ESCC. Further immune-related exploration may provide an opportunity for future therapeutic combination.

Construction and Evaluation of a Risk Score Model for Lymph Node Metastasis-Associated Circadian Clock Genes in Esophageal Squamous Carcinoma

Background: Studies suggested that circadian clock genes (CCGs) in human esophageal squamous carcinoma (ESCC) samples are dysregulated. However, the relevance of CCGs to lymph node metastasis (LNM) and prognosis of ESCC remains unclear. Methods: The differentially expressed genes (DEGs) between normal and ESCC samples in The Cancer Genome Atlas database (TCGA) database were intersected with the genes associated with LNM (LNMGs) in ESCC samples and 300 CCGs to obtain the differentially expressed LNM-associated CCGs (DE-LNM-CCGs). The risk model was constructed by Cox regression analysis in the TCGA-ESCC training set, and the accuracy of the risk model was verified by risk profile and overall survival profile. Furthermore, differences of 23 immune cells, 13 immune functions, and immune checkpoint molecules between the high- and low-risk groups were assessed using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Gene set enrichment analysis (GSEA) was conducted to investigate the functional differences between low- and high-risk groups. Finally, we validated the mRNA expression levels of prognostic model genes by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of six DE-LNM-CCGs were identified in TCGA-ESCC. TP53 and NAGLU were selected by Cox regression analysis to construct the risk model. Risk profile plots, overall survival plots, and validation results of the risk model in the validation set indicated that the constructed risk model was reliable. The result of ssGSEA showed that the percentages of activated B cells, activated dendritic cells, effector memory CD8 T cells, immune function in neutrophils, plasmacytoid dendritic cells, T cell co-inhibition, and Type 17 T helper cells were different between the high- and low-risk groups. In addition, the expression of CD274, PDCD1, TNFRSF18, and TNFRSF9 was dysregulated between the high- and low-risk groups. GSEA revealed that the high-risk group was associated with cell differentiation, oxidative phosphorylation, and steroid biosynthesis pathways, while the low-risk group was associated with chromosome, ECM–receptor interaction, and other pathways. Finally, qRT-PCR results showed that the mRNA expression levels of two prognostic genes were consistent with TCGA. Conclusion: In conclusion, the risk model constructed based on TP53 and NAGLU could accurately predict the prognosis.

The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing

A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell–cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.

The Key Clinical Questions of Neoadjuvant Chemoradiotherapy for Resectable Esophageal Cancer—A Review

Over 50% of individuals with esophageal cancer (EC) present with advanced stages of the disease; therefore, their outcome following surgery alone is poor, with only 25%–36% being alive 5 years post-surgery. Based on the evidence that the CROSS and NEOCRTEC5010 trials provided, neoadjuvant chemoradiotherapy (nCRT) is now the standard therapy for patients with locally advanced EC. However, there are still many concerning clinical questions that remain controversial such as radiation dose, appropriate patient selection, the design of the radiation field, the time interval between chemoradiotherapy (CRT) and surgery, and esophageal retention. With immune checkpoint inhibitors (ICIs) rapidly becoming a mainstay of cancer therapy, along with radiation, chemotherapy, and surgery, the combination mode of immunotherapy is also becoming a hot topic of discussion. Here, we try to provide constructive suggestions to answer the perplexing problems and clinical concerns for the progress of nCRT for EC in the future.

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

Purpose

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction.

Methods

Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (LASSO). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high- and low-risk groups. Afterwards, we performed CIBERSORT and the single-sample gene set enrichment analysis (ssGSEA) method to explore immune infiltration status. Furthermore, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, and immunotherapy efficacy.

Results

We constructed a robust necroptosis-related 22-lncRNA model, serving as an independent prognostic factor for breast cancer (BRCA). The low-risk group seemed to be the immune-activated type. Meanwhile, it showed that the higher the tumor mutation burden (TMB), the higher the riskScore. PD-L1-CTLA4 combined immunotherapy seemed to be a promising treatment strategy. Lastly, patients were assigned to 4 clusters to better discern the heterogeneity among patients.

Conclusions

The necroptosis-related lncRNA signature and molecular clusters indicated superior predictive performance in prognosis and the immune microenvironment, which may also provide guidance to drug regimens for immunotherapy and provide novel insights into precision medicine.

Comprehensive analysis of the importance of PLAUR in the progression and immune microenvironment of renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common type of kidney cancer with a high mortality rate, and the discovery of new therapeutic markers is essential to improve patient survival. The plasminogen activator urokinase receptor (PLAUR) plays key roles in tissue remodeling and extracellular matrix degradation, which contribute to invasion and metastasis, a major feature of tumor malignancy. The role of PLAUR in ccRCC pathology has not been deeply studied. In this study, we collected the mRNA expression data of 33 tumor types, each derived from human patients obtained from TCGA database, and comprehensively analyzed the correlation between the expression of PLAUR in tumors and prognosis. Then, we studied the relationship between PLAUR expression in ccRCC and specific clinical features of ccRCC patients. In addition, we analyzed the function and mechanism of PLAUR in ccRCC. Our results showed that PLAUR was significantly overexpressed in ccRCC and that both PLAUR levels and PLAUR methylation levels significantly correlated with poor prognosis. Our results also suggest that PLAUR is involved in the progression of ccRCC. The results of functional and mechanistic analysis of PLAUR showed that PLAUR is involved in inflammatory and immune-related pathways in ccRCC; other data showed that PLAUR expression may affect the infiltration of multiple immune cell types in ccRCC and that PLAUR levels were significantly and positively correlated with the expression of immune checkpoints. In conclusion, our findings suggest that high PLAUR expression can promote the progression of ccRCC to poor prognosis, and thus PLAUR may serve as both a potential marker for predicting macrophage infiltration and immune microenvironment status and as an important immunotherapy target for ccRCC.

Tumor Necrosis Factor Family Member Profile Predicts Prognosis and Adjuvant Chemotherapy Benefit for Patients With Small-Cell Lung Cancer

Tumor necrosis factor (TNF) family members participate in the body's antitumor immunity response and influence tumor prognosis and treatment response. However, little is known about the roles of TNF family members in small cell lung cancer (SCLC). Therefore, we conducted the first comprehensive investigation of TNF family members in patients with SCLC, with the goal of using them to predict prognosis and chemotherapy benefit. Abnormal genetic alterations and expression of TNF family members were found to be widespread in SCLC patients. Using LASSO Cox regression analysis, we constructed a TNF family-based signature that separated SCLC patients in the training set (n=77) into high- and low-risk groups with distinct survival and chemotherapy benefit, and the signature was well-validated in the validation set (n=137) by RT-qPCR. Importantly, the signature exhibited superior predictive performance and was identified as a novel independent prognostic factor. Additionally, different immune phenotypes were found between the low-risk and high-risk groups, and high-risk patients had higher CMTM6 expression, suggesting that these patients could benefit from therapeutic methods targeting CMTM6. We constructed the first clinically applicable TNF family-based signature for predicting prognosis and chemotherapy benefit for patients with SCLC. The findings reported here provide a new method for predicting the prognosis of SCLC patients and optimizing clinical management.

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation

Background and Purpose: Pathological response status is a standard reference for the early evaluation of the effect of neoadjuvant chemoradiation (nCRT) on locally advanced rectal cancer (LARC) patients. Various patients respond differently to nCRT, but identifying the pathological response of LARC to nCRT remains a challenge. Therefore, we aimed to identify a signature that can predict the response of LARC to nCRT. Material and Methods: The gene expression profiles of 111 LARC patients receiving fluorouracil-based nCRT were used to obtain gene pairs with within-sample relative expression orderings related to pathological response. These reversal gene pairs were ranked according to the mean decrease Gini index provided by the random forest algorithm to obtain the signature. This signature was verified in two public cohorts of 46 and 42 samples, and a cohort of 33 samples measured at our laboratory. In addition, the signature was used to predict disease-free survival benefits in a series of colorectal cancer datasets. Results: A 41-gene pair signature (41-GPS) was identified in the training cohort with an accuracy of 84.68% and an area under the receiver operating characteristic curve (AUC) of 0.94. In the two public test cohorts, the accuracy was 93.37 and 73.81%, with AUCs of 0.97 and 0.86, respectively. In our dataset, the AUC was 0.80. The results of the survival analysis show that 41-GPS plays an effective role in identifying patients who will respond to nCRT and have a better prognosis. Conclusion: The signature consisting of 41 gene pairs can robustly predict the clinical pathological response of LARC patients to nCRT.

RNA N6 -methyladenosine modification in the lethal teamwork of cancer stem cells and the tumor immune microenvironment: Current landscape and therapeutic potential

N6 -methyladenosine (m6 A), the newest and most prevalent layer of internal epigenetic modification in eukaryotic mRNA, has been demonstrated to play a critical role in cancer biology. Increasing evidence has highlighted that the interaction between cancer stem cells (CSCs) and the tumor immune microenvironment (TIME) is the root cause of tumorigenesis, metastasis, therapy resistance, and recurrence. In recent studies, the m6 A modification has been tightly linked to this CSC-TIME interplay, participating in the regulation of CSCs and TIME remolding. Interestingly, the m6 A modification has also been identified as a novel decisive factor in the efficacy of immunotherapies-particularly anti-PD-1/PD-L1 monotherapies-by changing the plasticity of the TIME. Given the functional importance of the m6 A modification in the crosstalk between CSCs and the TIME, targeting m6 A regulators will open new avenues to overcome therapeutic resistance, especially for immune checkpoint-based immunotherapy. In the present review, we summarize the current landscape of m6 A modifications in CSCs and the TIME, and also prospect the underling role of m6 A modifications at the crossroads of CSCs and the TIME for the first time. Additionally, to provide the possibility of modulating m6 A modifications as an emerging therapeutic strategy, we also explore the burgeoning inhibitors and technologies targeting m6 A regulators. Lastly, considering recent advances in m6 A-seq technologies and cancer drug development, we propose the future directions of m6 A modification in clinical applications, which may not only help to improve individualized monitoring and therapy but also provide enhanced and durable responses in patients with insensitive tumors.

Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma

Background

Few studies have addressed the role of immune-related genes in the survival and prognosis of different esophageal cancer (EC) sub-types. We established two new prognostic model indexes by bioinformatics analysis to select patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) who may benefit from immunotherapy.

Methods

Based on TCGA and ImmPort data sets, we screened immune genes differentially expressed between tumor and normal tissues in ESCC and EAC and analyzed the relationship between these genes and patient survival outcomes. We established the risk score models of immune-related genes in ESCC and EAC by multivariate COX regression analysis.

Results

We identified 12 and 11 immune-related differentially expressed genes associated with the clinical prognosis of ESCC and EAC respectively, based on which two prognostic risk score models of the two EC sub-types were constructed. It was found that the survival probability of patients with high scores was significantly lower than that of patients with low scores (p < 0.001). BMP1, EGFR, S100A12, HLA-B, TNFSF18, IL1B, MAPT and OXTR were significantly related to sex, TNM stage or survival outcomes of ESCC or EAC patients (p < 0.05). In addition, the risk score of ESCC was significantly correlated with the level of B cell infiltration in immune cells (p < 0.05).

Conclusions

The prognosis-related immune gene model indexes described herein prove to be useful prognostic biomarkers of the two EC sub-types in that they may provide a reference direction for looking for the beneficiaries of immunotherapy for EC patients.