Development of an Immune-Related Gene Signature for Prognosis in Melanoma

As a novel prognostic model for breast cancer, the identification and validation of telomere-related long noncoding RNA signatures

BACKGROUND

Telomeres are a critical component of chromosome integrity and are essential to the development of cancer and cellular senescence. The regulation of breast cancer by telomere-associated lncRNAs is not fully known, though. The goals of this study were to describe predictive telomere-related LncRNAs (TRL) in breast cancer and look into any possible biological roles for these RNAs.

METHODS

We obtained RNA-seq data, pertinent clinical data, and a list of telomere-associated genes from the cancer genome atlas and telomere gene database, respectively. We subjected differentially expressed TRLs to co-expression analysis and univariate Cox analysis to identify a prognostic TRL. Using LASSO regression analysis, we built a prognostic model with 14 TRLs. The accuracy of the model's prognostic predictions was evaluated through the utilization of Kaplan-Meier (K-M) analysis as well as receiver operating characteristic (ROC) curve analysis. Additionally, immunological infiltration and immune drug prediction were done using this model. Patients with breast cancer were divided into two subgroups using cluster analysis, with the latter analyzed further for variations in response to immunotherapy, immune infiltration, and overall survival, and finally, the expression of 14-LncRNAs was validated by RT-PCR.

RESULTS

We developed a risk model for the 14-TRL, and we used ROC curves to demonstrate how accurate the model is. The model may be a standalone prognostic predictor for patients with breast cancer, according to COX regression analysis. The immune infiltration and immunotherapy results indicated that the high-risk group had a low level of PD-1 sensitivity and a high number of macrophages infiltrating. In addition, we've discovered a number of small-molecule medicines with considerable for use in treating high-risk groups. The cluster 2 subtype showed the highest immune infiltration, the highest immune checkpoint expression, and the worst prognosis among the two subtypes defined by cluster analysis, which requires more attention and treatment.

CONCLUSION

As a possible biomarker, the proposed 14-TRL signature could be utilized to evaluate clinical outcomes and treatment efficacy in breast cancer patients.

Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma

PURPOSE

Melanoma is widely utilized as a prominent model for the development of immunotherapy, thought an inadequate immune response can occur. Moreover, the development of apoptosis-related therapies and combinations with other therapeutic strategies is impeded by the limited understanding of apoptosis's role within diverse tumor immune microenvironments (TMEs).

METHODS

Here, we constructed an apoptosis-related tumor microenvironment signature (ATM) and employ multi-dimensional analysis to understand the roles of apoptosis in tumor microenvironment. We further assessed the clinical applications of ATM in nine independent cohorts, and anticipated the impact of ATM on cellular drug response in cultured cells.

RESULTS

Our ATM model exhibits robust performance in survival prediction in multiple melanoma cohorts. Different ATM groups exhibited distinct molecular signatures and biological processes. The low ATM group exhibited significant enrichment in B cell activation-related pathways. What's more, plasma cells showed the lowest ATM score, highlighting their role as pivotal contributors in the ATM model. Mechanistically, the analysis of the interplay between plasma cells and other immune cells elucidated their crucial role in orchestrating an effective anti-tumor immune response. Significantly, the ATM signature exhibited associations with therapeutic efficacy of immune checkpoint blockade and the drug sensitivity of various agents, including FDA-approved and clinically utilized drugs targeting the VEGF signaling pathway. Finally, ATM was associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical "hot tumors".

CONCLUSION

Our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma.

Single-cell RNA sequencing unveils tumor heterogeneity and immune microenvironment between subungual and plantar melanoma

Acral melanoma (AM) is a subtype of melanoma with high prevalence in East Asians. AM is characterized by greater aggressiveness and lower survival rates. However, there are still fewer studies on immune mechanisms of AM especially subungual melanoma (SM) versus non-subungual melanoma (NSM). In order to explore tumor heterogeneity and immune microenvironment in different subtypes of AM, we applied single-cell RNA sequencing to 24,789 single cells isolated from the SM and plantar melanoma (PM) patients. Aspects of tumor heterogeneity, melanocytes from PM and SM had significant differences in gene expression, CNV and pathways in which tumor-associated such as NF-kb and Wnt were involved. Regarding the immune microenvironment, PM contained more fibroblasts and T/NK cells. The EPHA3-EFNA1 axis was expressed only in cancer-associated fibroblast (CAF) and melanocytes of PM, and the TIGIT-NECTIN2 axis was expressed in both AM subtypes of T/NK cells and melanocytes. Altogether, our study helps to elucidate the tumor heterogeneity in AM subpopulations and provides potential therapeutic targets for clinical research.

Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients

BACKGROUND/AIMS

The purpose of this study is to screen the feature genes related to gut microflora and explore the role of the genes in predicting the prognosis of patients with gastric cancer.

MATERIALS AND METHODS

We downloaded the gene profile of gastric cancer from the University of California Santa Cruz, the gut microflora related to gastric cancer from The Cancer Microbiome Atlas. The GSE62254 dataset was downloaded from National Center for Biotechnology Information Gene Expression Omnibus as a validation dataset. A correlation network between differentially expressed genes and gut microflora was constructed using Cytoscape. The optimized prognostic differentially expressed genes were identified through least absolute shrinkage and selection operator (LASSO) algorithm and univariate Cox regression analysis. The risk score model was established and then measured via Kaplan-Meier and area under the curve. Finally, the nomogram model was constructed according to the independent clinical factors, which was evaluated using C-index.

RESULTS

A total of 754 differentially expressed genes and 8 gut microflora were screened, based on which we successfully constructed the correlation network. We obtained 9 optimized prognostic differentially expressed genes, including HSD17B3, GNG7, CHAD, ARHGAP8, NOX1, YY2, GOLGA8A, DNASE1L3, and ABCA8. Moreover, Kaplan-Meier curves indicated the risk score model correctly predicted the prognosis of gastric cancer in both University of California Santa Cruz and GSE62254 dataset (area under the curve >0.8; area under the curve >0.7). Finally, we constructed the nomogram, in which the C index of 1, 3, and 5 years was 0.824, 0.772, and 0.735 representing that the nomogram was consistent with the actual situation.

CONCLUSIONS

These results indicate the 9 differentially expressed genes related to gut microflora might predict the survival time of patients with gastric cancer. Both risk signature and nomogram could effectively predict the prognosis for patients with gastric cancer.

Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer

Background

Breast cancer (BRCA) ranks first among cancers in terms of incidence and mortality rates in women, primarily owing to metastasis, chemo-resistance, and heterogeneity. To predict long-term prognosis and design novel therapies for BRCA, more sensitive markers need to be explored.

Methods

Data from 1089 BRCA patients were downloaded from TCGA database. Pearson's correlation analysis and univariate and multivariate Cox regression analyses were performed to assess the role of cell death-related genes (CDGs) in predicting BRCA prognosis. Kaplan-Meier survival curves were generated to compare the overall survival in the two subgroups. A nomogram was constructed using risk scores based on the five CDGs and other clinicopathological features. CCK-8, EdU incorporation, and colony formation assays were performed to verify the inhibitory effect of NFKBIA on BRCA cell proliferation. Transwell assay, flow cytometry, and immunohistochemistry analyses were performed to ascertain the biological function of NFKBIA.

Results

Five differentially expressed CDGs were detected among 156 CDGs. The risk score for each patient was then calculated based on the expression levels of the five CDGs. Distinct differences in immune infiltration, expression of immune-oncological targets, mutation status, and half-maximal inhibitory concentration values of some targeted drugs were observed between the high- and low-risk groups. Finally, in vitro cell experiments verified that NFKBIA overexpression suppresses the proliferation and migration of BRCA cells.

Conclusions

Our study revealed that some CDGs, especially NFKBIA, could serve as sensitive markers for predicting the prognosis of patients with BRCA and designing more personalized clinical therapies.

Prognostic model of kidney renal clear cell carcinoma using aging-related long noncoding RNA signatures identifies THBS1-IT1 as a potential prognostic biomarker for multiple cancers

Aging is responsible for the main intrinsic triggers of cancers; however, the studies of aging risk factors in cancer animal models and cancer patients are rare and insufficient to be represented in cancer clinical trials. For a better understanding of the complex regulatory networks of aging and cancers, 8 candidate aging related long noncoding RNAs (CarLncs) identified from the healthy aging models, centenarians and their offsprings, were selected and their association with kidney renal clear cell carcinoma (KIRC) was explored by series of cutting edge analyses such as support vector machine (SVM) and random forest (RF) algorithms. Using data downloaded from TCGA and GTEx databases, a regulatory network of CarLncs-miRNA-mRNA was constructed and five genes within the network were screened out as aging related feature genes for developing KIRC prognostic models. After a strict filtering pipeline for modeling, a formula using the transcript per million (TPM) values of feature genes "LncAging_score = 0.008* MMP11 + 0.066* THBS1-IT1 + (-0.014)* DYNLL2 + (-0.030)* RMND5A+ 0.008* PEG10" was developed. ROC analysis and nomogram suggest our model achieves a great performance in KIRC prognosis. Among the 8 CarLncs, we found that THBS1-IT1 was significantly dysregulated in 12 cancer types. A comprehensive pan-cancer analysis demonstrated that THBS1-IT1 is a potential prognostic biomarker in not only KIRC but also multiple cancers, such as LUSC, BLCA, GBM, LGG, MESO, PAAD, STAD and THCA, it was correlated with tumor microenvironment (TME) and tumor immune cell infiltration (TICI) and its high expression was related with poor survival.

TIGIT, a novel immune checkpoint therapy for melanoma

Melanoma is the most aggressive and deadliest type of skin cancer. In the last 10 years, immune checkpoint blockades (ICBs) including PD-1/PD-L1 and CTLA-4 inhibitor has been shown to be effective against melanoma. PD-1/PD-L1 and CTLA-4 inhibitors have shown varying degrees of drug resistance in the treatment of melanoma patients. Furthermore, the clinical benefits of ICBs are also accompanied by severe immune toxicity. Therefore, there is an urgent need to develop new immune checkpoint inhibitors to optimize melanoma therapy and reduce cytotoxicity. T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) is thought to activate inhibitory receptors in T cells, natural killer (NK) cells, and regulatory T cells (Tregs), and has become a promising target for immunotherapy. Studies have found that TIGIT can be detected in different stages of melanoma, which is closely related to the occurrence, development, and prognosis of melanoma. This review mainly describes the immunosuppressive mechanism of TIGIT and its role in antitumor immunity of melanoma, so as to provide new ideas and schemes for the clinical treatment of melanoma with targeted TIGIT.

Discovery and Validation of a SIT1-Related Prognostic Signature Associated with Immune Infiltration in Cutaneous Melanoma

Signaling threshold regulating transmembrane adaptor 1 (SIT1) encodes a disulfide-linked homodimeric lymphocyte-specific glycoprotein involved in immune cell activation. However, the relationship between SIT1 and the prognosis of skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes remains elusive. Here, we first compared the differences in SIT1 expression levels between SKCM tissues and adjacent normal tissues. Next, we found that the immune cell infiltration levels and signature pattern of immune infiltration were positively associated with the SIT1 gene mRNA levels. TCGA_SKCM RNA-seq data unveiled that the SIT1 upregulated several immune-associated signaling pathways in GSEA analysis. The high expression of SIT1 was closely related to improved survival in patients with SKCM. A pathway enrichment analysis of SIT1-associated immunomodulators indicated the involvement of the NF-κB signaling pathways. Based on SIT1-associated immunomodulators, we built a 13-gene signature by LASSO Cox regression which served as an independent prognostic factor for the survival of melanoma patients. By using the signature risk score, we achieved a good prediction result for the immunotherapy response and survival of SKCM patients. Our findings provided evidence for SIT1's implication in tumor immunity and survival of SKCM patients. The nominated immune signature is a promising predictive model for prognosis and immunotherapy sensitivity in SKCM patients.

Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma

Purpose

Colon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.

Methods

We obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

A total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.

Conclusion

The efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.

Identification of Crucial Gene Modules Related to the Efficiency of Anti-PD-1/PD-L1 Therapy and Comprehensive Analyses of a Novel Signature Based on These Modules

Biomarker development for clinical checkpoint inhibition is still in its early stages. It is critical to determine the cause of the lack of a long-term response in patients after immune checkpoint blockade (ICB) treatment and to develop composite biomarkers or signatures to improve personalized approaches. Three modules that were significantly correlated with the immunotherapeutic response were identified. Stimulatory pathways of cellular immunity, extracellular matrix formation-related pathways, and ATP metabolism-related pathways were enriched. Two distinct transcriptional subtypes were determined. Tumor microenvironment (TME) characteristics were highly correlated with “hot” and “cold” tumors. The ICB score was significantly correlated with clinical characteristics including age, Breslow depth, Clerk level, AJCC stage, and T stage. Meanwhile, a low ICB score is characterized by increased activation of immunity, a higher level of immune infiltration, and immune molecule expression. The ICB score showed a robust ability to predict melanoma prognosis in the discovery, internal validation, and external validation cohorts. In addition, a low ICB score was linked to a higher CR/PR rate in the immunotherapeutic cohort. The ICB score could reflect the pre-existing immune features and the expression pattern of “Cold” versus “Hot” tumors in melanoma patients. Thus, it has the potential to serve as a reliable predictor of melanoma prognosis and response to ICB therapy.

Transcriptome-Wide m6A Methylome and m6A-Modified Gene Analysis in Asthma

N6-methyladenosine (m6A) modification is one of the most prevalent RNA modification forms and is an important posttranscriptional mechanism for regulating genes. In previous research, we found that m6A regulator–mediated RNA methylation modification was involved in asthma; however, the specific modified genes are not clear. In this study, we systematically evaluated the transcriptome-wide m6A methylome and m6A-modified genes in asthma. Here, we performed two high-throughput sequencing methods, methylated RNA immunoprecipitation sequencing (MeRIP-seq), and RNA sequencing (RNA-seq) to identify key genes with m6A modification in asthma. Through difference analysis, we found that 416 methylation peaks were significantly upregulated and 152 methylation peaks were significantly downregulated, and it was mainly distributed in 3′ UTR. Furthermore, compared with the control group, there were 2,505 significantly upregulated genes and 4,715 significantly downregulated genes in the asthma group. Next, through a combined analysis of transcriptome and differential peaks, 14 differentially expressed genes related to RNA methylation modification were screened. Finally, through 87 health controls and 411 asthma cases from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) program, we verified three m6A-modified key genes (BCL11A, MATK, and CD300A) and found that they were mainly distributed in exons and enriched in 3' UTR. Our findings suggested that intervening in m6A-modified genes may provide a new idea for the treatment of asthma.

Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma

Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.

An Immune-Related Gene Pair Index Predicts Clinical Response and Survival Outcome of Immune Checkpoint Inhibitors in Melanoma

The durable responses and favorable long-term outcomes are limited to a proportion of advanced melanoma patients treated with immune checkpoint inhibitors (ICI). Considering the critical role of antitumor immunity status in the regulation of ICI therapy responsiveness, we focused on the immune-related gene profiles and aimed to develop an individualized immune signature for predicting the benefit of ICI therapy. During the discovery phase, we integrated three published datasets of metastatic melanoma treated with anti-PD-1 (n = 120) and established an immune-related gene pair index (IRGPI) for patient classification. The IRGPI was constructed based on 31 immune-related gene pairs (IRGPs) consisting of 51 immune-related genes (IRGs). The ROC curve analysis was performed to evaluate the predictive accuracy of IRGPI with AUC = 0.854. Then, we retrospectively collected one anti-PD-1 therapy dataset of metastatic melanoma (n = 55) from Peking University Cancer Hospital (PUCH) and performed the whole-transcriptome RNA sequencing. Combined with another published dataset of metastatic melanoma received anti-CTLA-4 (VanAllen15; n = 42), we further validated the prediction accuracy of IRGPI for ICI therapy in two datasets (PUCH and VanAllen15) with AUCs of 0.737 and 0.767, respectively. Notably, the survival analyses revealed that higher IRGPI conferred poor survival outcomes in both the discovery and validation datasets. Moreover, correlation analyses of IRGPI with the immune cell infiltration and biological functions indicated that IRGPI may be an indicator of the immune status of the tumor microenvironment (TME). These findings demonstrated that IRGPI might serve as a novel marker for treating of melanoma with ICI, which needs to be validated in prospective clinical trials.

The Prognostic and Predictive Role of Xeroderma Pigmentosum Gene Expression in Melanoma

Background

Assessment of immune-specific markers is a well-established approach for predicting the response to immune checkpoint inhibitors (ICIs). Promising candidates as ICI predictive biomarkers are the DNA damage response pathway genes. One of those pathways, which are mainly responsible for the repair of DNA damage caused by ultraviolet radiation, is the nucleotide excision repair (NER) pathway. Xeroderma pigmentosum (XP) is a hereditary disease caused by mutations of eight different genes of the NER pathway, or POLH, here together named the nine XP genes. Anecdotal evidence indicated that XP patients with melanoma or other skin tumors responded impressively well to anti-PD-1 ICIs. Hence, we analyzed the expression of the nine XP genes as prognostic and anti-PD-1 ICI predictive biomarkers in melanoma.

Methods

We assessed mRNA gene expression in the TCGA-SKCM dataset (n = 445) and two pooled clinical melanoma cohorts of anti-PD-1 ICI (n = 75). In TCGA-SKCM, we applied hierarchical clustering on XP genes to reveal clusters, further utilized as XP cluster scores. In addition, out of 18 predefined genes representative of a T cell inflamed tumor microenvironment, the TIS score was calculated. Besides these scores, the XP genes, immune-specific single genes (CD8A, CXCL9, CD274, and CXCL13) and tumor mutational burden (TMB) were cross-correlated. Survival analysis in TCGA-SKCM was conducted for the selected parameters. Lastly, the XP response prediction value was calculated for the two pooled anti-PD-1 cohorts by classification models.

Results

In TCGA-SKCM, expression of the XP genes was divided into two clusters, inversely correlated with immune-specific markers. A higher ERCC3 expression was associated with improved survival, particularly in younger patients. The constructed models utilizing XP genes, and the XP cluster scores outperformed the immune-specific gene-based models in predicting response to anti-PD-1 ICI in the pooled clinical cohorts. However, the best prediction was achieved by combining the immune-specific gene CD274 with three XP genes from both clusters.

Conclusion

Our results suggest pre-therapeutic XP gene expression as a potential marker to improve the prediction of anti-PD-1 response in melanoma.

A Pan-Cancer Analysis of Tumor-Infiltrating B Cell Repertoires

Tumor-infiltrating B cells can play an important role in anti-tumor responses but their presence is not well understood. In this study, we extracted the B cell receptor repertoires from 9522 tumor and adjacent non-tumor samples across 28 tumor types in the Cancer Genome Atlas project and performed diversity and network analysis. We identified differences in diversity and network statistics across tumor types and subtypes and observed a trend towards increased clonality in primary tumors compared to adjacent non-tumor tissues. We also found significant associations between the repertoire features and mutation load, tumor stage, and age. Our V-gene usage analysis identified similar V-gene usage patterns in colorectal and endometrial cancers. Lastly, we evaluated the prognostic value of the repertoire features and identified significant associations with survival in seven tumor types. This study warrants further research into better understanding the role of tumor-infiltrating B cells across a wide range of tumor types.

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.

High DSCC1 Level Predicts Poor Prognosis of Lung Adenocarcinoma

Purpose

To evaluate the role of DSCC1 in LUAD.

Patients and Methods

Based on TCGA and GTEx, the Wilcoxon rank-sum test was used to compare the expression differences of DSCC1 between the normal samples of GTEx combined TCGA and the unpaired tumor samples of TCGA, and to compare DSCC1 expression values between tumor tissues and paired normal LUAD tissues in the TCGA cohort. Kruskal–Wallis rank-sum test, Wilcoxon rank-sum test, and logistics regression were used to compare the relationship between the expression of DSCC1 and the clinicopathological parameters. The biological function of DSCC1 was annotated by GSEA and ssGSEA, while Kaplan–Meier and Cox regression analysis were used to evaluate the prognostic value of DSCC1. Furthermore, the time-dependent ROC curve was used to analyze the diagnostic efficacy of DSCC1 in LUAD.

Results

We downloaded the RNA-Seq data of 513 LUAD cases. The expression of DSCC1 was significantly correlated with T stage (OR = 1.04(1.02–1.07), P = 0.002), pathological stage (OR=1.03 (1.01–1.05), P = 0.008) and TP53 status (OR=1.10 (1.07–1.14), P < 0.001). The high expression of DSCC1 was significantly correlated with DSS (HR=1.56 (1.07–2.26), P = 0.021) and OS (HR=1.53 (1.14–2.05), P = 0.004). Moreover, ROC curve analysis (AUC=0.845, CI (0.820-0.870)) indicated DSCC1 as a potential diagnostic molecule for LUAD. In the group with high DSCC1 expression phenotype, down-regulation of EGFR signal, reduction of IL-6 deprivation, cell cycle, and p53 signal pathway were significantly abundant. Spearman correlation analysis showed that the expression of DSCC1 was positively correlated with the infiltration of Th2 cells, T Helper cells.

Conclusion

Our results suggest that DSCC1 may be an important biomarker for the treatment of LUAD.

Development and Validation of a CD8+ T Cell Infiltration-Related Signature for Melanoma Patients

Aim

Immunotherapy shows efficacy in only a subset of melanoma patients. Here, we intended to construct a risk score model to predict melanoma patients’ sensitivity to immunotherapy.

Methods

Integration analyses were performed on melanoma patients from high-dimensional public datasets. The CD8+ T cell infiltration related genes (TIRGs) were selected via TIMER and CIBERSORT algorithm. LASSO Cox regression was performed to screen for the crucial TIRGs. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to evaluate the immune activity. The prognostic value of the risk score was determined by univariate and multivariate Cox regression analysis.

Results

184 candidate TIRGs were identified in melanoma patients. Based on the candidate TIRGs, melanoma patients were classified into three clusters which were characterized by different immune activity. Six signature genes were further screened out of 184 TIRGs and a representative risk score for patient survival was constructed based on these six signature genes. The risk score served as an indicator for the level of CD8+ T cell infiltration and acted as an independent prognostic factor for the survival of melanoma patients. By using the risk score, we achieved a good predicting result for the response of cancer patients to immunotherapy. Moreover, pan-cancer analysis revealed the risk score could be used in a wide range of non-hematologic tumors.

Conclusions

Our results showed the potential of using signature gene-based risk score as an indicator to predict melanoma patients’ sensitivity to immunotherapy.