Immune-infiltration based signature as a novel prognostic biomarker in gastrointestinal stromal tumour

Density of tumor-infiltrating NK and Treg cells is associated with 5 years progression-free and overall survival in resected lung adenocarcinoma

Surgical resection of pulmonary adenocarcinoma is considered to be curative but progression-free survival (PFS) has remained highly variable. Antitumor immune response may be important, however, the prognostic significance of tumor-infiltrating natural killer (NK) and regulatory T (Treg) lymphocytes is uncertain. Resected pulmonary adenocarcinoma tissues (n = 115) were studied by immunohistochemical detection of NKp46 and FoxP3 positivity to identify NK and Treg cells, respectively. Association of cell densities with clinicopathological features and progression-free survival (PFS) as well as overall survival (OS) were analyzed with a follow-up time of 60 months. Both types of immune cells were accumulated predominantly in tumor stroma. NK cell density showed association with female gender, non-smoking and KRAS wild-type status. According to Kaplan-Meier analysis, PFS and OS proved to be longer in patients with high NK or Treg cell densities (p = 0.0293 and p = 0.0375 for PFS, p = 0.0310 and p = 0.0448 for OS, respectively). Evaluating the prognostic effect of the combination of NK and Treg cell density values revealed that PFS and OS were significantly longer in NKhigh/Treghigh cases compared to the other groups combined (p = 0.0223 and p = 0.0325, respectively). Multivariate Cox regression analysis indicated that high NK cell density was independent predictor of longer PFS while high NK and high Treg cell densities both proved significant predictors of longer OS. The NKhigh/Treghigh combination also proved to be an independent prognostic factor for both PFS and OS. In conclusion, NK and Treg cells can be components of the innate and adaptive immune response at action against progression of pulmonary adenocarcinoma.

Identification of TAP1 as a T-cell related therapeutic target in gastric cancer by mediating oxalipliatin-related synergistic enhancement of immunotherapy

BACKGROUND

Given the intricate molecular complexities and heterogeneity inherent in T-cell immunotherapy of gastric cancer (GC), elucidative T-cell-related biomarkers were imperative needed for facilitating the prediction of GC patient prognosis and identify potential synergistic therapeutic targets.

METHODS

We conducted COX regression analysis in TISIDB, TCGA-STAD, and GEO databases to identify 19 GC T-cell-mediated sensitivity tumor killing (TTK) genes (key GCTTKs). Based on key GCTTKs, we constructed two TTK patterns and analyzed their metabolic pathways, mutation features, clinical data distribution, immune cell infiltration, and prognosis. LASSO regression was performed to develop a T-cell-mediated GC Prognosis (TGCP) model. We validated the TGCP model in GC patients. TAP1 was further selected for investigation of its biological functions and molecular mechanisms. We assessed the potential of TAP1 as a promising therapeutic target for GC using Patient-derived organoids (PDOs)-derived xenografts (PDOXs) models of GC.

RESULTS

The TTK patterns display notable disparities. The TGCP model showcases its proficiency in predicting immune response efficacy, effectively distinguishes immunotherapy difference GC patients. Our findings find further confirmation in PDOX models, affirming TAP1 can enhance immunotherapy facilitated by PDL1 inhibitors. Furthermore, Oxaliplatin, by promoting TAP1 expression, augments PDL1 expression, thereby enhancing the efficacy of immunotherapy.

CONCLUSIONS

We constructed a TGCP model, which demonstrates satisfactory predictive accuracy. Out of 9 prognostic genes, TAP1 was validated as a synergistic target for Oxaliplatin and PDL1 inhibitors, offering a genetic-level explanation for the synergy observed in GC treatment involving Oxaliplatin in combination with PDL1 inhibitors.

The prognostic value of tumour-infiltrating lymphocytes, programmed cell death protein-1 and programmed cell death ligand-1 in Stage I-III triple-negative breast cancer

BACKGROUND

Tumour-infiltrating lymphocytes (TILs) represent a robust biological prognostic biomarker in triple-negative breast cancer (TNBC); however, the contribution of different subsets of immune cells is unclear. We investigated the prognostic value of immune markers, including stromal TILs (sTILs), CD8+T and FOPX3+T cells, PD-1 and PD-L1 in non-metastatic TNBC.

METHODS

In total, 259 patients with Stage I-III TNBC were reviewed. The density of sTILs along with the presence of total (t), stromal (s), and intratumoral (i) CD8+T cells and FOPX3+T cells were evaluated by haematoxylin and eosin and immunohistochemical staining. Immunohistochemical staining of PD-1, PD-L1 was also conducted.

RESULTS

All immune markers were positively correlated with each other (P < 0.05). In the multivariate analysis, sTILs (P = 0.046), tCD8+T cells (P = 0.024), iCD8+T cells (P = 0.050) and PD-1 (P = 0.039) were identified as independent prognostic factors for disease-free survival (DFS). Further analysis showed that tCD8+T cells (P = 0.026), iCD8+T cells (P = 0.017) and PD-1 (P = 0.037) increased the prognostic value for DFS beyond that of the classic clinicopathological factors and sTILs.

CONCLUSIONS

In addition to sTILs, inclusion of tCD8+T, iCD8+T cells, or PD-1 may further refine the prognostic model for non-metastatic TNBC beyond that including classical factors alone.

Advances in immunology and immunotherapy for mesenchymal gastrointestinal cancers

Mesenchymal gastrointestinal cancers are represented by the gastrointestinal stromal tumors (GISTs) which occur throughout the whole gastrointestinal tract, and affect human health and economy globally. Curative surgical resections and tyrosine kinase inhibitors (TKIs) are the main managements for localized GISTs and recurrent/metastatic GISTs, respectively. Despite multi-lines of TKIs treatments prolonged the survival time of recurrent/metastatic GISTs by delaying the relapse and metastasis of the tumor, drug resistance developed quickly and inevitably, and became the huge obstacle for stopping disease progression. Immunotherapy, which is typically represented by immune checkpoint inhibitors (ICIs), has achieved great success in several solid tumors by reactivating the host immune system, and been proposed as an alternative choice for GIST treatment. Substantial efforts have been devoted to the research of immunology and immunotherapy for GIST, and great achievements have been made. Generally, the intratumoral immune cell level and the immune-related gene expressions are influenced by metastasis status, anatomical locations, driver gene mutations of the tumor, and modulated by imatinib therapy. Systemic inflammatory biomarkers are regarded as prognostic indicators of GIST and closely associated with its clinicopathological features. The efficacy of immunotherapy strategies for GIST has been widely explored in pre-clinical cell and mouse models and clinical experiments in human, and some patients did benefit from ICIs. This review comprehensively summarizes the up-to-date advancements of immunology, immunotherapy and research models for GIST, and provides new insights and perspectives for future studies.

Mucins as Potential Biomarkers for Early Detection of Cancer

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

Analysis of m6A methylation patterns and tumor microenvironment in endometrial cancer

BACKGROUND

The N⁶-methyladenosine (m⁶A) modification is the most common epigenetic modification in eukaryotic mRNA. In recent years, lots of studies have shown that the tumor microenvironment (TME) plays a critical role in tumor growth and development. However, there are few studies on the interaction between m⁶A methylation and the TME in uterine corpus endometrial carcinoma (UCEC).

METHODS

Three distinct m⁶A modification patterns were based on 21 m⁶A regulators of UCEC patients and tumor-free individuals. We investigated the relationship between m⁶A modification patterns and associated features of the TME. Differentially expressed genes were selected and the m⁶A score was established to evaluate the prognosis and immunotherapeutic efficacy of UCEC patients.

RESULTS

We identified three different m⁶A modification patterns. The TME infiltrating characteristics were highly consistent with tumors with three distinct immune phenotypes. Besides, our analysis showed that the m⁶A score was shown to be useful in predicting clinical outcomes. Patients with the low m⁶A score seemed to have a better prognosis, a stronger immunotherapeutic response, and a higher tumor mutation burden.

CONCLUSION

Our study explored the influence of m⁶A modification and TME on the prognosis of cancer patients, which will contribute to the discovery of immunotherapy strategies to improve their prognosis.

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Background

The increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.

Methods

We downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.

Results

A total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.

Conclusion

Based on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-contrast MRI

RATIONALE AND OBJECTIVES

Conventional chemotherapy has limited benefit in pancreatic ductal adenocarcinoma (PDAC), necessitating identification of novel therapeutic targets. Radiomics may enable non-invasive prediction of CD20 expression, a hypothesized therapeutic target in PDAC. To develop a machine learning classifier based on noncontrast magnetic resonance imaging for predicting CD20 expression in PDAC.

MATERIALS AND METHODS

Retrospective study was conducted on preoperative noncontrast magnetic resonance imaging of 156 patients with pathologically confirmed PDAC from January 2017 to April 2018. For each patient, 1409 radiomics features were selected using minimum absolute contraction and selective operator logistic regression algorithms. CD20 expression was quantified using immunohistochemistry. A multilayer perceptron network classifier was developed using the training and validation set.

RESULTS

A log-rank test showed that the CD20-high group (22.37 months, 95% CI: 19.10-25.65) had significantly longer survival than the CD20-low group (14.9 months, 95% CI: 10.96-18.84). The predictive model showed good differentiation in training (area under the curve [AUC], 0.79) and validation (AUC, 0.79) sets. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 73.21%, 75.47%, 0.74, 0.76, and 0.73, respectively, for the training set and 69.23%, 80.95%, 0.74, 0.82, and 0.68, respectively, for the validation set.

CONCLUSION

Multilayer perceptron classifier based on noncontrast magnetic resonance imaging scanning can predict the level of CD20 expression in PDAC patients.

Correlation of Immunological and Molecular Profiles with Response to Crizotinib in Alveolar Soft Part Sarcoma: An Exploratory Study Related to the EORTC 90101 "CREATE" Trial

Alveolar soft part sarcoma (ASPS) is a rare subtype of soft tissue sarcoma characterized by an unbalanced translocation, resulting in ASPSCR1-TFE3 fusion that transcriptionally upregulates MET expression. The European Organization for Research and Treatment of Cancer (EORTC) 90101 “CREATE” phase II trial evaluated the MET inhibitor crizotinib in ASPS patients, achieving only limited antitumor activity. We performed a comprehensive molecular analysis of ASPS tissue samples collected in this trial to identify potential biomarkers correlating with treatment outcome. A tissue microarray containing 47 ASPS cases was used for the characterization of the tumor microenvironment using multiplex immunofluorescence. DNA isolated from 34 available tumor samples was analyzed to detect recurrent gene copy number alterations (CNAs) and mutations by low-coverage whole-genome sequencing and whole-exome sequencing. Pathway enrichment analysis was used to identify diseased-associated pathways in ASPS sarcomagenesis. Kaplan–Meier estimates, Cox regression, and the Fisher’s exact test were used to correlate histopathological and molecular findings with clinical data related to crizotinib treatment, aiming to identify potential factors associated with patient outcome. Tumor microenvironment characterization showed the presence of PD-L1 and CTLA-4 in 10 and 2 tumors, respectively, and the absence of PD-1 in all specimens. Apart from CD68, other immunological markers were rarely expressed, suggesting a low level of tumor-infiltrating lymphocytes in ASPS. By CNA analysis, we detected a number of broad and focal alterations. The most common alteration was the loss of chromosomal region 1p36.32 in 44% of cases. The loss of chromosomal regions 1p36.32, 1p33, 1p22.2, and 8p was associated with shorter progression-free survival. Using whole-exome sequencing, 13 cancer-associated genes were found to be mutated in at least three cases. Pathway enrichment analysis identified genetic alterations in NOTCH signaling, chromatin organization, and SUMOylation pathways. NOTCH4 intracellular domain dysregulation was associated with poor outcome, while inactivation of the beta-catenin/TCF complex correlated with improved outcome in patients receiving crizotinib. ASPS is characterized by molecular heterogeneity. We identify genetic aberrations potentially predictive of treatment outcome during crizotinib therapy and provide additional insights into the biology of ASPS, paving the way to improve treatment approaches for this extremely rare malignancy.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors

OBJECTIVES

To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected. All patients were divided into low-, moderate-, and high-risk groups according to the modified NIH classification criteria. All indicators were analyzed by univariate analysis. The indicators with inter-group differences were used to establish regression and decision tree models to predict the biological risk of GISTs.

RESULTS

There were statistically significant differences in long diameter, S/L ratio, internal echo level, echo homogeneity, boundary, shape, necrotic cystic degeneration, and blood flow signals among the low-, moderate-, and high-risk groups (all p < .05). The logistic regression model based on the echo homogeneity, shape, necrotic cystic degeneration and blood flow signals had an accuracy rate of 76.96% for predicting the biological risk, which was higher than the 72.77% of the decision tree model (based on the long diameter, the location of tumor origin, echo homogeneity, shape, and internal echo) (p = .008). In the low-risk and high-risk groups, the predicting accuracy rates of the regression model reached 87.34 and 81.82%, respectively.

CONCLUSIONS

Transabdominal ultrasound is highly valuable in predicting the biological risk of GISTs. The logistic regression model has greater predictive value than the decision tree model.

Fibrinogen-like protein 2 in gastrointestinal stromal tumour

Gastrointestinal stromal tumour (GIST), the most common sarcoma of the gastrointestinal tract, can be treated effectively with tyrosine kinase inhibitors, such as imatinib. Cancer immune therapy has limited efficacy, and little is known about the immune suppressive factors in GISTs. Fibrinogen‐like protein 2 (FGL2) is expressed either as a membrane‐associated protein or as a secreted soluble protein that has immune suppressive functions. We found that GISTs expressed FGL2 mRNA highly compared to other types of cancer in a large human cancer transcriptome database. GIST expressed FGL2 frequently also when studied using immunohistochemistry in two large clinical series, where 333 (78%) out of the 425 GISTs were FGL2 positive. The interstitial cells of Cajal, from which GISTs may originate, expressed FGL2. FGL2 expression was associated with small GIST size, low mitotic counts and low tumour‐infiltrating lymphocyte (TIL) counts. Patients whose GIST expressed FGL2 had better recurrence‐free survival than patients whose GIST lacked expression. Imatinib upregulated FGL2 in GIST cell lines, and the patients with FGL2‐negative GIST appeared to benefit most from long duration of adjuvant imatinib. We conclude that GISTs express FGL2 frequently and that FGL2 expression is associated with low TIL counts and favourable survival outcomes.

CRYAB predicts clinical prognosis and is associated with immunocyte infiltration in colorectal cancer

Background

αB-Crystallin (CRYAB) is differentially expressed in various tumors. However, the correlation between CRYAB and immune cell infiltration in colorectal cancer (CRC) remains unclear.

Materials & Methods

Kaplan-Meier survival curves in The Cancer Genome Atlas (TCGA) were used to evaluate the relationship between CRYAB expression and both overall survival and progression-free survival. The relationships between CRYAB expression and infiltrating immune cells and their corresponding gene marker sets were examined using the TIMER database.

Results

The expression of CRYAB was lower in CRC tumor tissues than in normal tissues (P < 0.05). High CRYAB gene expression and high levels of CRYAB gene methylation were correlated with high-grade malignant tumors and more advanced tumor, nodes and metastasis (TNM) cancer stages. In addition, in colorectal cancer, there was a positive correlation between CRYAB expression and immune infiltrating cells including neutrophils, macrophages, CD8 + T cells, and CD4 + T cells, as well as immune-related genes including CD2, CD3D, and CD3E. Methylation sites such as cg13084335, cg15545878, cg13210534, and cg15318568 were positively correlated with low expression of CRYAB.

Conclusion

Because CRYAB likely plays an important role in immune cell infiltration, it may be a potential tumor-suppressor gene in CRC and a potential novel therapeutic target and predictive biomarker for colorectal cancer (CRC).

An immune-related lncRNA signature predicts prognosis and adjuvant chemotherapeutic response in patients with small-cell lung cancer

Background

Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease’s dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC.

Methods

Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods.

Results

An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype.

Conclusion

We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02357-1.

Construction of a novel immune-related lncRNA signature and its potential to predict the immune status of patients with hepatocellular carcinoma

Background

The accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC.

Methods

In this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy.

Results

A total of 1009 pairs of DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients.

Conclusions

The nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09059-x.

Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA

Background

The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma.

Methods

The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy.

Results

After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome.

Conclusions

The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy.

A Signature-Based Classification of Gastric Cancer That Stratifies Tumor Immunity and Predicts Responses to PD-1 Inhibitors

Gastric cancer is a leading cause of cancer-related deaths with considerable heterogeneity among patients. Appropriate classifications are essential for prognosis prediction and individualized treatment. Although immunotherapy showed potential efficacy in a portion of patients with gastric cancer, few studies have tried to classify gastric cancer specifically based on immune signatures. In this study, we established a 3-subtype cluster with low (C_LIM), medium (C_MIM), and high (C_HIM) enrichment of immune signatures based on immunogenomic profiling. We validated the classification in multiple independent datasets. The C_HIM subtype exhibited a relatively better prognosis and showed features of “hot tumors”, including low tumor purity, high stromal components, overexpression of immune checkpoint molecules, and enriched tumor-infiltrated immune cells (activated T cells and macrophages). In addition, C_HIM tumors were also characterized by frequent ARID1A mutation, rare TP53 mutation, hypermethylation status, and altered protein expression (HER2, β-catenin, Cyclin E1, PREX1, LCK, PD-L1, Transglutaminase, and cleaved Caspase 7). By Gene Set Variation Analysis, “TGFβ signaling pathway” and “GAP junction” were enriched in C_LIM tumors and inversely correlated with CD8⁺ and CD4⁺ T cell infiltration. Of note, the C_HIM patients showed a higher response rate to immunotherapy (44.4% vs. 11.1% and 16.7%) and a more prolonged progression-free survival (4.83 vs. 1.86 and 2.75 months) than C_MIM and C_LIM patients in a microsatellite-independent manner. In conclusion, the new immune signature-based subtypes have potential therapeutic and prognostic implications for gastric cancer management, especially immunotherapy.

Identification and Validation of Immune-Related lncRNA Signature as a Prognostic Model for Skin Cutaneous Melanoma

Purpose

Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer that results in high morbidity and mortality rate worldwide. Immune-related long non-coding RNAs (IRlncRs) play an important role in regulating gene expression in tumors. Therefore, in this study, we aimed to identify IRlncRs signature that could predict prognosis and therapeutic targets for melanoma irrespective of the gene expression levels.

Methods

RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA). IRlncRs were identified using co-expression analysis and recognized using univariate analysis. The impact of IRlncRs on survival was analyzed using a modified least absolute shrinkage and selection operator (Lasso) regression model. A 1-year survival receiver operating characteristic curve was constructed, and the area under the curve was calculated to identify the optimal cut-off point to distinguish between high and low-risk groups in patients with SKCM. Furthermore, integrative analysis was performed to identify the impact of clinicopathological features, chemotherapeutic treatment, tumor-infiltrating immune cells, and mutant genes on survival.

Results

A total of 28 IRlncRs significantly associated with survival were identified. Seventeen IRlncRs pairs were used to build a survival risk model that could be used to distinguish between low and high-risk groups. The high-risk group was negatively associated with tumor-infiltrating immune cells and had a higher half inhibitory centration for chemotherapeutic agents such as cisplatin and vinblastine. Additionally, the high-risk group had a positive correlation with the expression of specific mutant genes such as BRAF and KIT.

Conclusion

Our findings demonstrate that some IRlncRs have a significant correlation with survival and therapeutic targets for SKCM patients and may provide new insight into the clinical diagnosis and treatment strategies for SKCM patients.

Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

Background

Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC.

Methods

In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model.

Results

Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers.

Conclusion

This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00966-3.

Single-cell transcriptome analysis revealed the heterogeneity and microenvironment of gastrointestinal stromal tumors

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the human gastrointestinal tract. In this study, we performed single-cell RNA sequencing (RNA-seq) on intra- and peri-tumor tissues from GIST patients with the aim of discovering the heterogeneity of tumor cells in GIST and their interactions with other cells. We found four predominating cell types in GIST tumor tissue, including T cells, macrophages, tumor cells, and NK cells. Tumor cells could be clustered into two groups: one was highly proliferating and associated with high risk of metastasis, the other seemed "resting" and associated with low risk. Their clinical relevance and prognostic values were confirmed by RNA-seq of 65 GIST samples. T cells were the largest cell type in our single-cell data. Two groups of CD8⁺ effector memory (EM) cells were in the highest clonal expansion and performed the highest cytotoxicity but were also the most exhausted among all T cells. A group of macrophages were found polarized to possess both M1 and M2 signatures, and increased along with tumor progression. Cell-to-cell interaction analysis revealed that adipose endothelial cells had high interactions with tumor cells to facilitate their progression. Macrophages were at the center of the tumor microenvironment, recruiting immune cells to the tumor site and having most interactions with both tumor and nontumor cells. In conclusion, we obtained an overview of the GIST microenvironment and revealed the heterogeneity of each cell type and their relevance to risk classifications, which provided a novel theoretical basis for learning and curing GISTs.

Prognostic value of a modified Immunoscore in patients with stage IIII resectable colon cancer

Objective

The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer. However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore (IS-mod) system for predicting overall survival (OS) in patients with stage I-III colon cancer.

Methods

The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort (N=212) and validation cohort (N=103) from two centers were used to evaluate the prognostic value of the IS-mod.

Results

In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS [adjusted hazard ratio (HR)=0.36, 95% confidence interval (95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like (IS-like) system (C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25% (C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.

Conclusions

Our method simplifies the annotation and accelerates the calculation of Immunoscore method, thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set (N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.

Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma

The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.