Immunoscore Predicts Survival in Early-Stage Lung Adenocarcinoma Patients

Prognostic Impact and Spatial Interplay of Immune Cells in Urothelial Cancer

BACKGROUND AND OBJECTIVE

Quantity and the spatial relationship of specific immune cell types can provide prognostic information in bladder cancer. The objective of the study was to characterize the spatial interplay and prognostic role of different immune cell subpopulations in bladder cancer.

METHODS

A total of 2463 urothelial bladder carcinomas were immunostained with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry in a tissue microarray format and analyzed using a framework of neuronal networks for an image analysis. Spatial immune parameters were compared with histopathological parameters and overall survival data.

KEY FINDINGS AND LIMITATIONS

The identification of > 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. Thirty-nine immune parameters showed prognostic significance in univariate analyses, of which 16 were independent from pT, pN, and histological grade in muscle-invasive bladder cancer. Among all these parameters, the strongest association with prolonged overall survival was identified for intraepithelial CD8+ cytotoxic T cells (time-dependent area under receiver operating characteristic curve [AUC]: 0.70), while stromal CD8+ T cells were less relevant (AUC: 0.65). A favorable prognosis of inflamed cancers with high levels of "exhaustion markers" suggests that TIM3, PD-L1, PD-1, and CTLA-4 on immune cells do not hinder antitumoral immune response in tumors rich of tumor infiltrating immune cells.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The density of intraepithelial CD8+ T cells was the strongest prognostic feature in muscle-invasive bladder cancer. Given that tumor cell killing by CD8+ cytotoxic T lymphocytes through direct cell-to-cell-contacts represents the "terminal end route" of antitumor immunity, the quantity of "tumor cell adjacent CD8+ T cells" may constitute a surrogate for the efficiency of cancer recognition by the immune system that can be measured straightaway in routine pathology as the CD8 labeling index.

PATIENT SUMMARY

Quantification of intraepithelial CD8+ T cells, the strongest prognosticfeature identified in muscle-invasive bladder cancer, can easily be assessed by brightfield immunohistochemistry and is therefore "ready to use" for routine pathology.

Principles of risk assessment in colon cancer: immunity is key

In clinical practice, the administration of adjuvant chemotherapy (ACT) following tumor surgical resection raises a critical dilemma for stage II colon cancer (CC) patients. The prognostic features used to identify high-risk CC patients rely on the pathological assessment of tumor cells. Currently, these factors are considered for stratifying patients who may benefit from ACT at early CC stages. However, the extent to which these factors predict clinical outcomes (i.e. recurrence, survival) remains highly controversial, also uncertainty persists regarding patients' response to treatment, necessitating further investigation. Therefore, an imperious need is to explore novel biomarkers that can reliably stratify patients at risk, to optimize adjuvant treatment decisions. Recently, we evaluated the prognostic and predictive value of Immunoscore (IS), an immune digital-pathology assay, in stage II CC patients. IS emerged as the sole significant parameter for predicting disease-free survival (DFS) in high-risk patients. Moreover, IS effectively stratified patients who would benefit most from ACT based on their risk of recurrence, thus predicting their outcomes. Notably, our findings revealed that digital IS outperformed the visual quantitative assessment of the immune response conducted by expert pathologists. The latest edition of the WHO classification for digestive tumor has introduced the evaluation of the immune response, as assessed by IS, as desirable and essential diagnostic criterion. This supports the revision of current cancer guidelines and strongly recommends the implementation of IS into clinical practice as a patient stratification tool, to guide CC treatment decisions. This approach may provide appropriate personalized therapeutic decisions that could critically impact early-stage CC patient care.

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

A Novel Notch-Related Gene Signature for Prognosis and Immune Response Prediction in Ovarian Cancer

Background and Objectives: Notch is a fascinating signaling pathway. It is extensively involved in tumor growth, cancer stem cells, metastasis, and treatment resistance and plays important roles in metabolic regulation, tumor microenvironment, and tumor immunity. However, the role of Notch in ovarian cancer (OC) has yet to be fully understood. Therefore, this study systematically described the expression, mutation, and copy number variation of genes in the Notch signaling pathway in OC and evaluated the relationship between gene mutation and Overall Survival (OS) prognosis. Materials and Methods: Notch risk score (NTRS) was established by univariate Cox regression analysis combined with Lasso regression analysis, and the efficacy of NTRS in predicting prognosis and immunotherapy response in patients with OC was verified. We further assessed the correlations of NTRS with clinical features, immune infiltration level, immune checkpoint expression, and immune characteristics. Additionally, differential expression and functions of the fourteen signature genes were confirmed via vitro assays. Results: The results showed that Notch genes (NTGs) were markedly differentiated between tumor and normal tissues, which may help to explain the high heterogeneity in the biological characteristics and therapeutic outcomes of human OC. A Notch risk (NTR) prognostic model based on 11 key NTGs was successfully constructed. Tumors with high Notch risk scores (NTRS) were independently associated with shorter overall survival and poorer immunotherapy outcomes. We further assessed the correlations of NTRS with immune characteristics. The results showed that NTGs play a key role in regulating the tumor immune microenvironment. Additionally, we validated the baseline and induced expressions of 14 prognosis-related NTGs in our own OC samples. In vitro assays confirmed that the knockdown of NCOR2 and APH1B and overexpression of HEY2 and SKP2 could inhibit the proliferation, invasion, and migration of OC cells. Conclusions: These findings emphasize that Notch multilayer changes are associated with the prognosis of patients with OC and the characteristics of immune cell infiltration. Our predictive signature may predict the prognosis and immunotherapy response of OC patients in an independent manner. NCOR2, APH1B, HEY2, and SKP2 may more prominently represent important indicators to improve patient prognosis.

A Signature Constructed Based on the Integrin Family Predicts Prognosis and Correlates with the Tumor Microenvironment of Patients with Lung Adenocarcinoma

As an important element in regulating the tumor microenvironment (TME), integrin plays a key role in tumor progression. This study aimed to establish prognostic signatures to predict the overall survival and identify the immune landscape of patients with lung adenocarcinoma based on integrins. The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus datasets were used to obtain information on mRNA levels and clinical factors (GSE72094). The least absolute shrinkage and selection operator (LASSO) model was used to create a prediction model that included six integrin genes. The nomogram, risk score, and time-dependent receiver operating characteristic analysis all revealed that the signatures had a good prognostic value. The gene signatures may be linked to carcinogenesis and TME, according to a gene set enrichment analysis. The immunological and stromal scores were computed using the ESTIMATE algorithm, and the data revealed, the low-risk group had a higher score. We discovered that the B lymphocytes, plasma, CD4+ T, dendritic, and mast cells were much higher in the group with low-risk using the CiberSort. Inflammatory processes and several HLA family genes were upregulated in the low-risk group. The low-risk group with a better prognosis is more sensitive to immune checkpoint inhibitor medication, according to immunophenoscore (IPS) research. We found that the patients in the high-risk group were more susceptible to chemotherapy than other group patients, according to the prophetic algorithm. The gene signatures could accurately predict the prognosis, identify the immune status of patients with lung adenocarcinoma, and provide guidance for therapy.

Spatially variant immune infiltration scoring in human cancer tissues

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients’ tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors’ immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients’ tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor’s immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

Human papillomavirus infection and lung adenocarcinoma: special benefit is observed in patients treated with immune checkpoint inhibitors

BACKGROUND

Human papilloma virus (HPV) has been associated with the development and modulation of response in a series of neoplasms. In the case of lung adenocarcinoma, its role in etiology and pathogenesis is still controversial. Considering that this infection brings foreign epitopes, it could be of prognostic significance in patients with lung adenocarcinoma treated with immunotherapy.

METHODS

In a retrospective cohort study we evaluated the presence of HPV genomic material in lung adenocarcinoma primary lesions with the INNO-LiPA platform. Viral replication was also evaluated by detecting the presence of oncoprotein E6/E7 messenger RNA (mRNA) by quantitative RT-PCR. To confirm possible hypotheses regarding viral oncogenesis, vascular endothelial growth factor (VEGF) and hypoxia-inducible factor 1 (HIF1) were evaluated with stromal fibrosis and immunoscore.

RESULTS

A total of 133 patients were included in the analysis, of whom 34 tested positive for HPV, reaching an estimated prevalence of 25.6% [95% confidence interval (CI) 18.2% to 32.9%]. E6/7 mRNA was identified in 28 out of the 34 previously positive cases (82.3%). In immune checkpoint inhibitor (ICI)-treated patients, the median overall survival reached 22.3 months [95% CI 19.4 months- not reached (NR)] for HPV-negative and was not reached in HPV-positive (HPV+) ones (95% CI 27.7-NR; P = 0.008). With regard to progression-free survival, HPV- patients reached a median of 9.2 months (95% CI 7.9-11.2 months) compared to 14.3 months (95% CI 13.8-16.4 months) when HPV was positive (P = 0.001). The overall response rate for HPV+ patients yielded 82.4% compared to 47.1% in negative ones. No differences regarding programmed death-ligand 1, VEGF, HIF1, stromal fibrosis, or immunoscore were identified.

CONCLUSIONS

In patients with HPV+ lung adenocarcinoma, a significant benefit in overall response and survival outcomes is observed.

Immune Score Predicts Outcomes of Gastric Cancer Patients Treated with Adjuvant Chemoradiotherapy

Background

Substantial evidence has demonstrated that tumor-infiltrating lymphocytes (TILs) are correlated with patient prognosis. The TIL-based immune score (IS) affects prognosis in various cancers, but its prognostic impact in gastric cancer (GC) patients treated with adjuvant chemoradiotherapy remains unclear.

Methods

A total of 101 GC patients who received chemoradiotherapy after gastrectomy were retrospectively analyzed in this study. Immunohistochemistry staining for CD3+ and CD8+ T-cell counts in both tumor center (CT) and invasive margin (IM) regions was built into the IS. Patients were then divided into three groups based on their differential IS levels. The correlation between IS and clinical parameters was analyzed. The prognostic impact of IS and clinical parameters was evaluated using Kaplan-Meier analysis and Cox proportional hazard regression analysis. Receiver operating characteristic (ROC) curves were plotted to compare the area under the curve (AUC) of IS with other clinical parameters. Nomograms for disease-free survival (DFS) and overall survival (OS) prediction were constructed based on the identified parameters.

Results

Finally, 20 (19.8%), 57 (56.4%), and 24 (23.8%) GC patients were identified with low, intermediate, and high IS levels, respectively. GC patients with higher IS levels exhibited better DFS (p < 0.001) and OS (p < 0.001). IS was an independent prognostic factor for both DFS (p < 0.001) and OS (p < 0.001) in multivariate analysis. IS presented a better predictive ability than the traditional pathological tumor-node-metastasis (pTNM) staging system (AUC: 0.801 vs. 0.677 and 0.800 vs. 0.660, respectively) with respect to both DFS and OS. The C-index of the nomograms for DFS and OS prediction was 0.737 and 0.774, respectively.

Conclusions

IS is a strong predictive factor for both DFS and OS in GC patients treated with adjuvant chemoradiotherapy, which may complement the traditional pTNM staging system.

Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade

In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

Tumor immune microenvironment is influenced by frameshift mutations and tumor mutational burden in gastric cancer

PURPOSE

Immunoscore can effectively predict prognosis in patients with colon cancer; however, its clinical application is limited. We modified the Immunoscore and created a tumor immune microenvironment (TIM) classification system for gastric carcinoma. Unlike previous studies that used small sample sizes or focused on particular immune-cell subtypes, our simplified system enables pathologists to classify gastric carcinomas intuitively using H&E-stained sections.

METHODS

Samples from 326 patients with advanced gastric carcinoma were reviewed and analyzed by pathologists using simple determination and digital image analysis. Comprehensive results of cancer-panel sequencing, Epstein-Barr‒virus (EBV) status, and PD-L1, HER2, ATM, PTEN, MET, FGFR2, and EGFR immunohistochemistry were evaluated with respect to the TIM class.

RESULTS

The TIM was classified as "hot" (n = 22), "immunosuppressed" (n = 178), "excluded" (n = 83), or "cold" (n = 43). TIM category was significantly associated with numbers of frameshift mutations (P < 0.001) and high tumor mutational burden (P < 0.004), and predicted overall survival. It was also significantly associated with age, histological type, degree of fibrosis, PD-L1 expression, loss of ATM and PTEN expression (P < 0.001), sex, EBV positivity, and HER2 overexpression (P < 0.04). "Hot" tumors were frequent in PD-L1 expressing and EBV-positive samples, and in those with ATM and PTEN loss. "Excluded" tumors were frequent in HER2-positive cases, whereas "cold" tumors were more frequent in younger patients with poorly cohesive histology and high fibrosis levels.

CONCLUSIONS

TIM classification system for gastric carcinoma has prognostic significance and results in classes that are associated with molecular characteristics.

Review of the recent clinical trials for PD-1/PD-L1 based lung cancer immunotherapy

INTRODUCTION

Lung cancer is known for its high mortality rate and prevalence in the world today. For decades, chemotherapy has been used as the main treatment for this cancer, but this has changed over time. Immune checkpoint inhibitors (ICIs) such as programmed death 1 and programmed death-ligand 1 (PD-1/PD-L1) blocking agents have been assessed in numerous clinical trials as single or combination therapy and have shown overall promising results. Nevertheless, various challenges have been encountered, which cast doubts over this method.

AREAS COVERED

We provide an introduction to the mechanisms underlying the PD-1/PD-L1 pathway. Then, we discuss the latest results from the most leading-edge studies evaluating PD-1/PD-L1 inhibitors in different lines of lung cancer therapy (some of which have gained FDA approval), potential biomarkers, and major challenges of ICI therapy.

EXPERT OPINION

Currently, the standard of care (SoC) for lung cancer consists mostly of chemotherapeutics. With further studies and ongoing trials evaluating novel ICI therapy, FDA has been approving specific ICI therapeutics, including PD-1/PD-L1 inhibitors, for particular types of lung cancer. However, for ICIs to play a key role in SoC, we need to overcome the major challenges of ICI therapy.

Identification of a methylomics-associated nomogram for predicting overall survival of stage I-II lung adenocarcinoma

The aim of this paper was to identify DNA methylation based biomarkers for predicting overall survival (OS) of stage I–II lung adenocarcinoma (LUAD) patients. Methylation profile data of patients with stage I–II LUAD from The Cancer Genome Atlas (TCGA) database was used to determine methylation sites-based hallmark for stage I–II LUAD patients’ OS. The patients were separated into training and validation datasets by using median risk score as cutoff. Univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to develop a DNA methylation signature for OS of patients with stage I–II LUAD. As a result, an 11-DNA methylation signature was determined to be critically associated with the OS of patients with stage I–II LUAD. Analysis of receiver operating characteristics (ROC) suggested a high prognostic effectiveness of the 11-DNA methylation signature in patients with stage I–II LUAD (AUC at 1, 3, 5 years in training set were (0.849, 0.879, 0.831, respectively), validation set (0.742, 0.807, 0.904, respectively), entire TCGA dataset (0.747, 0.818, 0.870, respectively). Kaplan–Meier survival analyses exhibited that survival was significantly longer in the low-risk cohort compared to the high-risk cohort in the training dataset (P = 7e − 07), in the validation dataset (P = 1e − 08), and in the all-cohort dataset (P = 6e − 14). In addition, a nomogram was developed based on molecular factor (methylation risk score) as well as clinical factors (age and cancer status) (AUC at 1, 3, 5 years entire TCGA dataset were 0.770, 0.849, 0.979, respectively). The result verified that our methylomics-associated nomogram had a strong robustness for predicting stage I–II LUAD patients’ OS. Furthermore, the nomogram combined clinical and molecular factors to determine an individualized probability of recurrence for patients with stage I–II LUAD, which stood for a major advance in the field of personalized medicine for pulmonary oncology. Collectively, we successfully identified a DNA methylation biomarker and a DNA methylation-based nomogram to predict the OS of patients with stage I–II LUAD.

Immune landscape and a promising immune prognostic model associated with TP53 in early-stage lung adenocarcinoma

Purpose

TP53 mutation, one of the most frequent mutations in early‐stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early‐stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early‐stage LUAD and formulate a TP53‐associated immune prognostic model (IPM) that can estimate prognosis in early‐stage LUAD patients.

Materials and methods

Immune‐associated differentially expressed genes (DEGs) between TP53 mutated (TP53^MUT) and TP53 wild‐type (TP53^WT) early‐stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune‐associated DEGs. We constructed and validated an IPM based on the TCGA and a meta‐GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.

Results

TP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early‐stage LUAD. Sixty‐seven immune‐associated DEGs were determined between TP53^WT and TP53^MUT cohort. An IPM consisting of two prognostic immune‐associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta‐GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43–3.06, p < 0.001). Increased expressions of PD‐L1, CTLA‐4, and TIGIT were revealed in the high‐risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.

Conclusion

The IPM based on TP53 status is a reliable and robust immune signature to identify early‐stage LUAD patients with high risk of unfavorable survival.

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Adenocarcinoma is the most common type of lung cancer, and patients have varying prognoses. RNA-binding proteins (RBP) are deemed to be closely associated with tumorigenesis and development, but the exact mechanism is currently unknown. This study was aimed at constructing a new robust prognostic model based on RNA-binding protein-related gene pair scores for better clinical guidance. The model for this study was constructed based on data of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Prognosis-related RBP gene pair models were created based on differentially expressed genes, and the accuracy of the models was verified in a different age, staging, and other subdatasets. A total of 379 RNA-binding protein-related genes were differentially expressed in tumor tissue. From these genes, we constructed a prognostic model consisting of 33 gene pairs, which were found to be significantly associated with survival in TCGA dataset (P < 0.0001, hazard ratio (HR) = 4.380 (3.139 to 6.111)) and different subdatasets. As expected, the results were verified in the GEO validation cohort (P = 7.8 × 10⁻³, HR = 1.597 (1.095 to 2.325)). We found that the signature exhibited an independent prognostic factor in both the univariate and multivariate Cox regression analyses (P < 0.001). CIBERSORT was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. CD8 T cells, activated dendritic cells, regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group (P < 0.01). Patients in the high-risk group had significantly higher tumor mutational burden (TMB) (P = 4.953e − 04) and lower levels of immune cells (P = 3.473e − 05) and stromal cells (P = 0.005) in the tumor microenvironment than those in the low-risk group. Furthermore, the Protein-protein interaction (PPI) network and various enrichment analyses have genuinely uncovered the interrelationships and potential functions of the RBP genes within the model. The results of the present study validated the importance of RNA-binding proteins in tumorigenesis and progression and support the RBP gene-related signature as a promising marker for prognosis prediction in lung adenocarcinoma.

Identification of the Prognostic Value of Immune-Related Genes in Esophageal Cancer

Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among EC patients. In this study, we downloaded the transcriptome profiling data and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database and found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p < 0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the “ImmPort” database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal survival-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by using Lasso regression and multivariate Cox regression analyses. Finally, we used those survival-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.