A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer

Prognostic value and tumour microenvironment characteristics of the Glasgow Microenvironment Score in primary triple-negative breast cancer

AIMS

The Glasgow Microenvironment Score (GMS) reflects the tumour microenvironment (TME) status by combining inflammatory cell infiltration and the tumour-stroma percentage. This study aimed to investigate the prognostic value and TME characteristics of the GMS for patients with triple-negative breast cancer (TNBC).

METHODS

A total of 123 patients with stage I-III TNBC were enrolled in this study. The association between GMS and clinicopathological characteristics was examined using the Pearson's χ2 test or Fisher's exact test. Kaplan-Meier plots were used to compare survival among the three GMS groups. Cox regression analyses were conducted to test the HR. Microenvironment Cell Populations-counter algorithm was used to estimate the TME components of each case.

RESULTS

We found that higher GMS score tended to exhibit the lower nuclear grade (p=0.016), more positive lymph nodes (p=0.014) and later tumour, node, metastases stage (p=0.012). GMS was an independent prognostic factor for disease-free survival in TNBC, and GMS 2 showed the worst prognosis (HR=6.42, p=0.028). GMS 0 was more infiltrated with cytotoxic lymphocytes, including CD8+ T cells (p=0.037) and natural killer cells (p=0.005), while GMS 2 was enriched in more endothelial cells (p=0.014) and fibroblasts (p=0.008).

CONCLUSION

Our study suggested that the GMS is a prognostic indicator for patients with TNBC. As an accessible and effective index, the GMS may be a promising tool to help clinicians assess prognostic risk and TME for patients with TNBC.

Integrative analysis of co-expression pattern of solute carrier transporters reveals molecular subtypes associated with tumor microenvironment hallmarks and clinical outcomes in colon cancer

Recent findings have suggested that solute carrier (SLC) transporters play an important role in tumor development and progression, and alterations in the expression of individual SLC genes are critical for fulfilling the heightened metabolic requirements of cancerous cells. However, the global influence of the co-expression pattern of SLC transporters on the clinical stratification and characteristics of the tumor microenvironment (TME) remains unexplored. In this study, we identified five SLC gene subtypes based on transcriptome co-expression patterns of 187 SLC transporters by consensus clustering analysis. These subtypes, which were characterized by distinct TME and biological characteristics, were successfully employed for prognostic and chemotherapy response prediction in colon cancer patients, as well as demonstrated associations with immunotherapy benefits. Then, we generated an SLC score model comprising 113 genes to quantify SLC gene co-expression patterns and validated it as an independent prognostic factor and drug response predictor in several independent colon cancer cohorts. Patients with a high SLC score possessed distinct characteristics of copy number variation, genomic mutations, DNA methylation, and indicated an SLC-S2 subtype, which was characterized by strong stromal cell infiltration, stromal pathway activation, poor prognosis, and low predicted fluorouracil and immunotherapeutic responses. Furthermore, the analysis of the Cancer Therapeutics Response Portal database revealed that inhibitors targeting PI3K catalytic subunits could serve as promising chemosensitizing agents for individuals exhibiting high SLC scores. In conclusion, the co-expression patterns of SLC transporters aided the disease classification, and the SLC score proved to be a reliable tool for distinguishing SLC gene subtypes and guiding precise treatment in patients with colon cancer.

Expression pattern of secretory-cell-related transcriptional signatures in colon adenocarcinomas defines tumor microenvironment characteristics and correlates with clinical outcomes

Despite the connection of secretory cells to distinct mucus-containing colon cancer histological subtypes and the interaction of secretory cells with immune cells in the pathogenesis of intestinal inflammatory diseases, whether the secretory cell signatures are associated with tumor microenvironment (TME) heterogeneity and can aid in colon cancer patient classification have not been investigated. Here, by performing the principal component analysis and consensus clustering analysis, we identified four distinct expression patterns based on secretory cell signatures which were significantly associated with different clinical behaviors, TME landscape, pathway activation, genomic mutations, and DNA methylation characteristics. Subsequently, a 'SCS score' model was constructed. The high SCS score indicated a pattern of 'secretory cell subtype 2', which was characterized by stromal infiltration and activation, and predicted poor prognosis and low sensitivity to fluorouracil-based chemotherapy and immunotherapy, but high sensitivity to PI3K catalytic subunit inhibitors. In conclusion, our study comprehensively uncovered the tumor heterogeneity related to secretory cell signature expression patterns. Moreover, the SCS score can supplement routine histopathological assessments to guide personalized therapeutic strategies in colon cancer patients.

Expression and Prognostic Value of CD80 and CD86 in the Tumor Microenvironment of Newly Diagnosed Glioblastoma

BACKGROUND

Strategies to modulate the tumor microenvironment (TME) have opened new therapeutic avenues with dramatic yet heterogeneous intertumoral efficacy in multiple cancers, including glioblastomas (GBMs). Therefore, investigating molecular actors of TME may help understand the interactions between tumor cells and TME. Immune checkpoint proteins such as a Cluster of Differentiation 80 (CD80) and CD86 are expressed on the surface of tumor cells and infiltrative tumor lymphocytes. However, their expression and prognostic value in GBM microenvironment are still unclear.

METHODS

In this study, we investigated, in a retrospective local discovery cohort and a validation TCGA dataset, expression of CD80 and CD86 at mRNA level and their prognostic significance in response to standard of care. Furthermore, CD80 and CD86 at the protein level were investigated in the discovery cohort.

RESULTS

Both CD80 and CD86 are expressed heterogeneously in the TME at mRNA and protein levels. In a univariate analysis, the mRNA expression of CD80 and CD86 was not significantly correlated with OS in both local OncoNeuroTek dataset and TCGA datasets. CD80 and CD86 mRNA high expression was significantly associated with shorter progression free survival (PFS) (p < 0.05). These findings were validated using the TCGA cohort; higher CD80 and CD86 expressions were correlated with shorter PFS (p < 0.05). In multivariate analysis, CD86 mRNA expression was an independent prognostic factor for PFS in the TCGA dataset only (p < 0.05).

CONCLUSION

CD86 could be used as a potential biomarker for the prognosis of GBM patients treated with immunotherapy; however, additional studies are needed to validate these findings.

A large-scale screening and functional sorting of tumour microenvironment prognostic genes for breast cancer patients

PURPOSE

The aim of this study was to systematically establish a comprehensive tumour microenvironment (TME)-relevant prognostic gene and target miRNA network for breast cancer patients.

METHODS

Based on a large-scale screening of TME-relevant prognostic genes (760 genes) for breast cancer patients, the prognostic model was established. The primary TME prognostic genes were selected from the constructing database and verified in the testing database. The internal relationships between the potential TME prognostic genes and the prognosis of breast cancer patients were explored in depth. The associated miRNAs for the TME prognostic genes were generated, and the functions of each primary TME member were investigated in the breast cancer cell line.

RESULTS

Compared with sibling controls, breast cancer patients showed 55 differentially expressed TME prognostic genes, of which 31 were considered as protective genes, while the remaining 24 genes were considered as risk genes. According to the lambda values of the LASSO Cox analysis, the 15 potential TME prognostic genes were as follows: ENPEP, CCDC102B, FEZ1, NOS2, SCG2, RPLP2, RELB, RGS3, EMP1, PDLIM4, EPHA3, PCDH9, VIM, GFI1, and IRF1. Among these, there was a remarkable linear internal relationship for CCDC102B but non-linear relationships for others with breast cancer patient prognosis. Using the siRNA technique, we silenced the expression of each TME prognostic gene. Seven of the 15 TME prognostic genes (NOS2, SCG2, RGS3, EMP1, PDLIM4, PCDH9, and GFI1) were involved in enhancing cell proliferation, destroying cell apoptosis, promoting cell invasion, or migration in breast cancer. Six of them (CCDC102B, RPLP2, RELB, EPHA3, VIM, and IRF1) were favourable for maintaining cell invasion or migration. Only two of them (ENPEP and FEZ1) were favourable for the processes of cell proliferation and apoptosis.

CONCLUSIONS

This integrated study hypothesised an innovative TME-associated genetic functional network for breast cancer patients. The external relationships between these TME prognostic genes and the disease were measured. Meanwhile, the internal molecular mechanisms were also investigated.

Clinical significance of STING expression and methylation in lung adenocarcinoma based on bioinformatics analysis

The role of stimulator of interferon genes [STING, also known as transmembrane protein 173 (TMEM173)] in various human cancers has begun to emerge. However, the clinical value of STING in lung adenocarcinoma (LUAD) remains elusive. This study aims to elucidate the clinical significance of STING expression and methylation in LUAD. Here, through analyzing data from public resources, we found that both the mRNA and protein expression of STING were reduced in lung cancer. Moreover, lower expression of STING was associated with a worse prognosis in LUAD, but not lung squamous cell carcinoma (LUSC). Of note, higher methylation of STING was found in LUAD and had the potential to distinguish LUAD tissues from adjacent non-tumor lung tissues and correlated with unfavorable outcomes. Furthermore, the methylation of STING could serve as an independent prognostic indicator for both the overall survival (OS) and disease-free survival (DFS) of LUAD patients. Additionally, the constructed nomogram exhibited a favorable predictive accuracy in predicting the probability of 1- and 2-year OS. Our findings suggest that the mRNA expression, and especially the DNA methylation of STING, have the potential to be prognostic indicators for LUAD patients.

Multi-omics characterization of the unsaturated fatty acid biosynthesis pathway in colon cancer

The biosynthesis of unsaturated fatty acids is involved in the initiation and progression of colon adenocarcinoma (COAD). In this study, we aimed to investigate the multi-omics characteristics of unsaturated fatty acid biosynthesis-related genes and explore their prognostic value in colon cancer by analyzing the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. An unsaturated fatty acid biosynthesis pathway related-genes enrichment score (BUFAS) was constructed utilizing the single sample gene set enrichment analysis (ssGSEA). We discovered that a high BUFAS was associated with longer overall survival (OS) in both the training and the validation sets. Multivariable analysis including the clinical characteristics further verified the independent prognostic value of the BUFAS in both the TCGA-COAD and the GSE39582 datasets. In addition, GSEA analysis revealed that BUFAS was positively associated with several signaling pathways, including MTORC1, peroxisome, and pathways related to fatty acid metabolism, while was negatively associated with other signaling pathways, such as hedgehog, NOTCH, and Wnt/beta-catenin pathway. Furthermore, in the COAD cell lines of the Genomics of Drug Sensitivity in Cancer (GDSC) database, we found that BUFAS was positively correlated with the drug sensitivities of cisplatin, gemcitabine, camptothecin, lapatinib, and afatinib, while was negatively correlated with that of ponatinib. Moreover, in the COAD single-cell transcriptomic dataset (GSE146771), the BUFAS varied among different cell types and was enriched in mast cells and fibroblasts. Taken together, the BUFAS we constructed could be used as an independent prognostic signature in predicting the OS and drug resistance of colon cancer. Unsaturated fatty acid biosynthesis pathway might serve as potential therapeutic targets for cancer treatment.

Hepatic Tumor Stiffness Measured by Shear Wave Elastography Is Prognostic for HCC Progression Following Treatment With Anti-PD-1 Antibodies Plus Lenvatinib: A Retrospective Analysis of Two Independent Cohorts

Background

The clinical significance of liver stiffness (LS) measured by shear wave elastography (SWE) in programmed cell death protein-1 (PD-1) inhibitors treated advanced hepatocellular carcinoma (HCC) patients remains unknown. This study aimed to explore the prognostic value of baseline LS by SWE prior to PD-1 inhibitor treatment in combination with lenvatinib.

Methods

We retrospectively evaluated patients (n=133) with HCC who received anti-PD-1 antibodies plus lenvatinib at two high-volume medical centres, between January 2020 and June 2021. Univariate and multivariate logistic regression analysis were used to develop a novel nomogram. RNA sequencing and immunohistochemical staining were used to assess the heterogeneity of biological and immune characteristics associated with tumor stiffness.

Results

The objective response rate (ORR) and disease control rate (DCR) of the whole population were 23.4% and 72.2%, respectively. A LS value of the baseline tumorous foci of 19.53 kPa had the maximum sum of sensitivity and specificity, making it the optimal cut-off value for predicting PD-1 inhibitor efficacy. The nomogram comprised baseline tumor LS and albumin-bilirubin grade (ALBI), which provided favorable calibration and discrimination in the training dataset with an AUC of 0.840 (95%CI: 0.750-0.931) and a C-index of 0.828. Further, it showed acceptable discrimination in the validation cohort, with an AUC of 0.827 (95%CI: 0.673-0.980) and C-index of 0.803. The differentially expressed genes enriched in high stiffness tumors were predominantly associated with metabolic pathways, while those enriched in low stiffness tumors were related to DNA damage repair. Furthermore, patients with high stiffness tumors had a relatively lower infiltration of immune cells and histone deacetylase pathway inhibitors were identified as candidate drugs to promote the efficacy of immunotherapy.

Conclusions

Baseline LS value of tumorous foci by SWE—that is, before administration of a PD-1 inhibitor in combination with lenvatinib—is a convenient predictor of PD-1 inhibitor efficacy in patients with advanced HCC, which has potential to be used for pretreatment stratification to optimize treatment of advanced HCC.

A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma

Background

Ferroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes.

Methods

RNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets.

Results

Seven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways.

Conclusion

In summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.

Cross talk between acetylation and methylation regulators reveals histone modifier expression patterns posing prognostic and therapeutic implications on patients with colon cancer

Background

Alterations in histone modifications have been reported to be related to tumorigenicity and tumor progression. However, whether histone modification can aid the classification of patients or influence clinical behavior in patients with colon cancer remains unclear. Therefore, this study aimed to evaluate histone modifier expression patterns using the unsupervised clustering of the transcriptomic expressions of 88 histone acetylation and methylation regulators.

Results

In this study, by consensus clustering analysis based on the transcriptome data of 88 histone modification regulators, we identified four distinct expression patterns of histone modifiers associated with different prognoses, intrinsic fluorouracil sensitivities, biological pathways, and tumor microenvironment characteristics among 1372 colon cancer samples. In these four clusters, the HMC4 cluster represented a stroma activation phenotype characterized by both the worst prognosis and lowest response rates to fluorouracil treatment. Then, we established a scoring scheme comprising 155 genes designated as “HM_score” by using the Boruta algorithm to distinguish colon cancer patients within the HMC4 cluster. Patients with a high HM_score were considered to have high stromal pathway activation, high stromal fraction, and an unfavorable prognosis. Further analyses indicated that a high HM_score also correlated with reduced therapeutic benefits from fluorouracil chemotherapy. Moreover, through CRISPR library screening, ZEB2 was found to be a critical driver gene that mediates fluorouracil resistance, which is associated with histone modifier expression patterns.

Conclusions

This study highlights that characterizing histone modifier expression patterns may help better understand the epigenetic mechanisms underlying tumor heterogeneity in patients with colon cancer and provide more personalized therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01290-y.

Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer

Background

Glycerolipid metabolism is involved in the genesis and progression of colon cancer. The current study aims at exploring the prognostic value and potential molecular mechanism of glycerolipid metabolism-related genes in colon cancer from the perspective of multi-omics.

Methods

Clinical information and mRNA expression data of patients with colon cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the glycerolipid metabolism-related gene enrichment score (GLMS). Univariable and multivariable Cox regression analyses were used to study the prognostic value of GLMS in TCGA-COAD and GSE39582 cohorts. The molecular mechanism of the prognostic factor was investigated via immune cell infiltration estimation and correlation analysis of cancer hallmark pathways. Single-cell transcriptomic dataset GSE146771 was used to identify the cell populations which glycerolipid metabolism targeted on.

Results

The GLMS was found to be associated with tumor location and consensus molecular types (CMSs) of colon cancer in TCGA-COAD cohort (P < 0.05). Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42-0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43-0.76). The association between the GLMS and OS remained significant in the multivariable analysis (TCGA cohort: P = 0.04; HR, 0.64; 95% CI, 0.42-0.98; GSE39582 cohort: P < 0.001; HR, 0.60; 95% CI, 0.45-0.80). The GLMS was positively correlated with cancer hallmark pathways including bile acid metabolism, xenobiotic metabolism, and peroxisome and negatively correlated with pathways such as interferon gamma response, allograft rejection, apoptosis, and inflammatory response (P < 0.05). Increased immune infiltration and upregulated expression of immune checkpoints were observed in patients with lower GLMS (P < 0.05). Single-cell datasets verified the different distribution of GLMS in cell subsets, with significant enrichment of GLMS in malignant cells and Tprolif cells.

Conclusion

We demonstrated that GLMS was a potential independent prognostic factor for colon cancer. The GLMS was also correlated with several cancer hallmark pathways, as well as immune microenvironment.

Tumor Microenvironment Evaluation for Gastrointestinal Cancer in the Era of Immunotherapy and Machine Learning

A dynamic and mutualistic interplay between tumor cells and the surrounding tumor microenvironment (TME) triggered the initiation, progression, metastasis, and therapy response of solid tumors. Recent clinical breakthroughs in immunotherapy for gastrointestinal cancer conferred considerable attention to the estimation of TME, and the maturity of next-generation sequencing (NGS)-based technology contributed to the availability of increasing datasets and computational toolbox for deciphering TME compartments. In the current review, we demonstrated the components of TME, multiple methodologies involved in TME detection, and prognostic and predictive TME signatures derived from corresponding methods for gastrointestinal cancer. The TME evaluation comprises traditional, radiomics, and NGS-based high-throughput methodologies, and the computational algorithms are comprehensively discussed. Moreover, we systemically elucidated the existing TME-relevant signatures in the prognostic, chemotherapeutic, and immunotherapeutic settings. Collectively, we highlighted the clinical and technological advances in TME estimation for clinical translation and anticipated that TME-associated biomarkers may be promising in optimizing the future precision treatment for gastrointestinal cancer.

OAS3 is a Co-Immune Biomarker Associated With Tumour Microenvironment, Disease Staging, Prognosis, and Treatment Response in Multiple Cancer Types

2′,5′-oligoadenylate synthase (OAS) is a class of enzymes induced by interferons and mainly encoded by the OAS1, OAS2, and OAS3 genes, which activate the potential RNA enzymes to degrade viral mRNA, inhibit viral protein synthesis and promote apoptosis in virus-infected cells. OAS3 is associated with breast cancer prognosis. However, the expression and prognosis of OAS3 and tumour-infiltrating lymphocytes in pan-cancer remain unknown. In the present study, we have systematically investigated and confirmed the role of OAS3 in tumour immune infiltration, immune escape, tumour progression, response to treatment, and prognosis of different cancer types using various bioinformatics methods. The findings suggest that OAS3 is aberrantly expressed in almost all TCGA cancer types and subtypes and is associated with tumour staging, metastasis, and prognostic deterioration in different tumours. In addition, OAS3 expression is associated with the prognosis and chemotherapeutic outcomes of various cancers. In terms of immune-infiltrating levels, OAS3 expression is positively associated with the infiltration of immunosuppressive cells. These findings suggest that OAS3 is correlated with prognosis and immune-infiltrating levels.

Evaluation of stromal cell infiltration in the tumor microenvironment enable prediction of treatment sensitivity and prognosis in colon cancer

Current clinical factors for screening candidates that might benefit from adjuvant chemotherapy in colon cancer are inadequate. Tumor microenvironment, especially the stromal components, has the potential to determine treatment response. However, clinical translation of the tumor-associated stromal characterization into a practical biomarker for helping treatment decision has not been established. Using machine learning, we established a novel 31-gene signature, called stromal cell infiltration intensity score (SIIS), to distinguish patients characterized by the enrichment of abundant stromal cells in five colon cancer datasets from GEO (N = 990). Patients with high-SIIS were at higher risk for recurrence and mortality, and could not benefit from adjuvant chemotherapy due to their intrinsic drug resistance; however, the opposite was reported for patients with low-SIIS. The role of SIIS in detection of patients with high stromal cell infiltration and reduced drug efficiency was consistently validated in the TCGA-COAD cohort (N = 382), Sun Yat-sen University Cancer Center cohort (N = 30), and could also be observed in TCGA pan-cancer settings (N = 4898) and four independent immunotherapy cohorts (N = 467). Based on multi-omics data analysis and the CRISPR library screen, we reported that lack of gene mutation, hypomethylation in ADCY4 promoter region, activation of WNT-PCP pathway and SIAH2-GPX3 axis were potential mechanisms responsible for the chemoresistance of patients within high-SIIS group. Our findings demonstrated that SIIS provide an important reference for those making treatment decisions for such special patients.

Immune-related Gene-based Prognostic Signature for the Risk Stratification Analysis of Breast Cancer

Background:

Breast cancer has plagued women for many years and caused many deaths around the world.

Method:

In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study.

Results:

Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion:

Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma

Due to the lack of effective diagnostic markers and therapeutic targets, esophageal squamous cell carcinoma (ESCC) shows a poor 5 years survival rate of less than 30%. To explore the potential therapeutic targets of ESCC, we integrated and reanalyzed the mutation data of WGS (whole genome sequencing) or WES (whole exome sequencing) from a total of 1,145 samples in 7 large ESCC cohorts, including 270 ESCC gene expression data. Two new mutation signatures and 20 driver genes were identified in our study. Among them, AP3S1, MUC16, and RPS15 were reported for the first time. We also discovered that the KMT2D was associated with the multiple clinical characteristics of ESCC, and KMT2D knockdown cells showed enhanced cell migration and cell invasion. Furthermore, a few neoantigens were shared between ESCC patients. For ESCC, compared to TMB, neoantigen might be treated as a better immunotherapy biomarker. Our research expands the understanding of ESCC mutations and helps the identification of ESCC biomarkers, especially for immunotherapy biomarkers.

LncRNAs Associated with Chemoradiotherapy Response and Prognosis in Locally Advanced Rectal Cancer

BACKGROUND

There are only limited studies on the long non-coding RNAs (lncRNAs) associated with neoadjuvant chemoradiotherapy (NCRT) response and prognosis of locally advanced rectal cancer (LARC) patients. This study identified lncRNAs associated with NCRT response and prognosis in CRC patients and explored their potential predictive mechanisms.

METHODS

The study subjected the LncRNA expression profiles from our previous gene chip data to LASSO and identified a four-lncRNA signature that predicted NCRT response and prognosis. A Cox regression model was subsequently performed to identify the prognostic risk factors. The function of LINC00909, the lncRNA with the most powerful predictive ability, was finally identified in vivo and in vitro using CRC cell lines.

RESULTS

A comparison of the relative lncRNA expression of NCRT-responsive and non-responsive patients revealed four hub lncRNAs: DBET, LINC00909, FLJ33534, and HSD52 with AUC = 0.68, 0.73, 0.73, and 0.70, respectively (all p < 0.05). COX regression analysis further demonstrated that DBET, LINC00909 and FLJ33534 were associated with the DFS in CRC patients. The expression of the four lncRNAs was also significant in LARC patients who had not undergone NCRT (all p < 0.05). A risk score model was subsequently constructed based on the results of the multivariate COX analysis and used to predict NCRT response and prognosis in the CRC and LARC patients. The expression and prognosis of DBET, LINC00909 and FLJ33534 in the CRC tissues were further validated in the R2 platform and Oncomine database. Notably, overexpression of the LINC00909 increased the cell line resistance to the 5-FU and radiotherapy in vivo and in vitro.

CONCLUSION

DBET, LINC00909, and FLJ33534 are potential novel biomarkers for predicting NCRT response and prognosis in CRC patients. In particular, LINC00909 is an effective oncogene in CRC that could be used as a novel therapeutic target to enhance NCRT response.

A Robust Panel Based on Mitochondrial Localized Proteins for Prognostic Prediction of Lung Adenocarcinoma

Due to high energy and material metabolism requirements, mitochondria are frequently active in tumor cells. Our study found that the high energy metabolism status is positively correlated with the poor prognosis of patients with lung adenocarcinoma. We constructed a scoring system (mitoRiskscore) based on the gene expression of specific mitochondrial localized proteins through univariate and LASSO cox regression. It has been shown that high mitoRiskscore was correlated with a shorter survival time after surgery in patients with lung adenocarcinoma. Compared with the typical TNM grading system, the mitoRiskscore gene panel had higher prediction accuracy. A vast number of external verification results ensured its universality. Additionally, the mitoRiskscore could evaluate the metabolic pattern and chemotherapy sensitivity of the tumor samples. Lung adenocarcinoma with higher mitoRiskscore was more active in glycolysis, and oxidative phosphorylation expression of proliferation-related pathway genes was also significantly upregulated. In contrast, patients with low mitoRiskscore had similar metabolic patterns to normal tissues. In order to improve the accuracy of prediction ability and promote clinical usage, we developed a nomogram that combined mitoRiskscore and clinical prognostic factors to predict the 3-year, 5-year, and 10-year survival rates of patients. We also performed in vitro experiments to verify the function of the key genes in the mitoRiskscore panel. In conclusion, the mitoRiskscore scoring system may assist clinicians to judge the postoperative survival rate and chemotherapy of patients with lung adenocarcinoma.

Transcriptomics based multi-dimensional characterization and drug screen in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) remains one of the deadly cancer types. Comprehensively dissecting the molecular characterization and the heterogeneity of ESCC paves the way for developing more promising therapeutics.

METHODS

Expression profiles of multiple ESCC datasets were integrated. ATAC-seq and RNA-seq were combined to reveal the chromatin accessibility features. A prognosis-related subtype classifier (PrSC) was constructed, and its association with the tumor microenvironment (TME) and immunotherapy was assessed. The key gene signature was validated in clinical samples. Based on the TME heterogeneity of ESCC patients, potential subtype-specific therapeutic agents were screened.

FINDINGS

The common differentially expressed genes (cDEGs) in ESCC were identified. Up-regulated genes (HEATR1, TIMELESS, DTL, GINS1, RUVBL1, and ECT2) were found highly important in ESCC cell survival. The expression alterations of PRIM2, HPGD, NELL2, and TFAP2B were associated with chromatin accessibility changes. PrSC was a robust scoring tool that was not only associated with the prognosis of ESCC patients, but also could reflect the TME heterogeneity. TNS1^high fibroblasts were associated with immune exclusion. TG-101348 and Vinorelbine were identified as potential subtype-specific therapeutic agents. Besides, the application of PrSC into two immunotherapy cohorts indicated its potential value in assessing treatment response to immunotherapy.

INTERPRETATION

Our study depicted the multi-dimensional characterization of ESCC, established a robust scoring tool for the prognosis assessment, highlighted the role of TNS1^high fibroblasts in TME, and identified potential drugs for clinical use.

FUNDING

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Prognostic signature of ovarian cancer based on 14 tumor microenvironment-related genes

BACKGROUND

Ovarian cancer is one of the lethal gynecological diseases in women. However, using tumor microenvironment related genes to identify prognostic signature of ovarian cancer has not been discussed in detail.

METHODS

The mRNA profiles of 386 ovarian cancer patients were retrieved from The Cancer Genome Atlas. Univariate Cox regression and LASSO Cox regression analyses were performed and 14 optimized prognostic genes related to tumor microenvironment were identified.

RESULTS

The multivariate Cox hazards regression showed risk score was an independent prognostic signature for ovarian cancer. Nomogram model could reliably predict the patients' survival. Furthermore, M1 macrophages, M2 macrophages, and follicular helper T cells, differentially expressed between the high- and low-risk groups, were found to be associated with the risk score.

CONCLUSION

CTL-associated antigen 4 (CTLA4) and indoleamine 2,3-Dioxygenase 1 (IDO1), which were previously shown to be important immune checkpoints, probably contribute to the immunosuppressive microenvironment aberration. This study may shed light on the prognosis of ovarian cancer.

A Robust Prognostic Gene Signature Based on eRNAs-Driven Genes in Prostate Cancer

Prostate cancer (PCa) is the second most common malignancy in men, but its exact pathogenetic mechanisms remain unclear. This study explores the effect of enhancer RNAs (eRNAs) in PCa. Firstly, we screened eRNAs and eRNA -driven genes from The Cancer Genome Atlas (TCGA) database, which are related to the disease-free survival (DFS) of PCa patients;. screening methods included bootstrapping, Kaplan–Meier (KM) survival analysis, and Pearson correlation analysis. Then, a risk score model was established using multivariate Cox analysis, and the results were validated in three independent cohorts. Finally, we explored the function of eRNA-driven genes through enrichment analysis and analyzed drug sensitivity on datasets from the Genomics of Drug Sensitivity in Cancer database. We constructed and validated a robust prognostic gene signature involving three eRNA-driven genes namely MAPK15, ZNF467, and MC1R. Moreover, we evaluated the function of eRNA-driven genes associated with tumor microenvironment (TME) and tumor mutational burden (TMB), and identified remarkable differences in drug sensitivity between high- and low-risk groups. This study identified a prognostic gene signature, which provides new insights into the role of eRNAs and eRNA-driven genes while assisting clinicians to determine the prognosis and appropriate treatment options for patients with PCa.

Signature based on metabolic-related gene pairs can predict overall survival of osteosarcoma patients

BACKGROUND

Osteosarcoma is a tumour of malignant origin in children and adolescents. Recent progression indicates that it is necessary to develop new therapies to improve the patient's prognosis rather than strengthen anti-tumour chemotherapy. Researchers recently realised that cancer is a kind of disease with a metabolic disorder, and metabolic reprogramming is becoming a new cancer hallmark. Hence, our study's primary purpose is to explore the value of genes related to osteosarcoma metabolism.

METHODS

From public databases, three osteosarcoma datasets with adequate clinical information were obtained. Besides, the IMvigor dataset through the 'IMvigor' package as a supplement was downloaded, the metabolic-related genes were identified, and these genes were used to construct the metabolic-related gene pairs (MRGP). Based on the prognosis-related MRGP, two molecular subtypes were identified. There are significant differences in the metabolic characteristics between the two molecular subtypes. Subsequently, the MRGP signature is constructed using the least absolute shrinkage and selection operator regression method. Finally, use SubMap analysis to evaluate the response of patients in the MRPG signature group to immunotherapy.

RESULTS

The MRGP signature can reliably predict overall survival in patients with osteosarcoma. The MRGP signature is also associated with osteosarcoma patients' metastatic status and can be used for subsequent risk classification of metastatic patients. The immunotherapy is more likely to benefit the patients in the MRGP low-risk group.

CONCLUSION

Metabolic-related gene pairs signature can assess the prognosis of patients with osteosarcoma.

A nine-gene signature to improve prognosis prediction of colon carcinoma

This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (-0.1214) * TNFRSF11A + (-0.2617) * TMEM97 + (-0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (-0.1312) * CXCL13 + (-0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age<65) and stages (tumor_stage I/II, tumor_stage III/IV, T3&T4, N0&N1, N2&N3, M0). This study provides a robust model for the prognosis prediction of colon carcinoma, and lays a basis for researching the molecular mechanism underlying the development of colon carcinoma.

Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Background

Identify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.

Materials and Methods

One RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.

Results

This signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.

Conclusion

The novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

Improving the Prognosis of Colon Cancer through Knowledge-Based Clinical-Molecular Integrated Analysis

Background

Colon cancer has high morbidity and mortality rates among cancers. Existing clinical staging systems cannot accurately assess the prognostic risk of colon cancer patients. This study was aimed at improving the prognostic performance of the colon cancer clinical staging system through knowledge-based clinical-molecular integrated analysis.

Methods

374 samples from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset were used as the discovery set. 98 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset were used as the validation set. After converting gene expression data into pathway dysregulation scores (PDSs), the random survival forest and Cox model were used to identify the best prognostic supplementary factors. The corresponding clinical-molecular integrated prognostic model was built, and the improvement of prognostic performance was assessed by comparing with the clinical prognostic model.

Results

The PDS of 14 pathways played important roles in prognostic prediction together with clinical prognostic factors through the random survival forest. Further screening with the Cox model revealed that the PDS of the pathway hsa00532 was the best clinical prognostic supplementary factor. The integrated prognostic model constructed with clinical factors and the identified molecular factor was superior to the clinical prognostic model in discriminative performance. Kaplan-Meier (KM) curves of patients grouped by PDS suggested that patients with a higher PDS had a poorer prognosis, and stage II patients could be distinctly distinguished.

Conclusions

Based on the knowledge-based clinical-molecular integrated analysis, a clinical-molecular integrated prognostic model and corresponding nomogram for colon cancer overall survival prognosis was built, which showed better prognostic performance than the clinical prognostic model. The PDS of the pathway hsa00532 is a considerable clinical prognostic supplementary factor for colon cancer and may represent a potential prognostic marker for stage II colon cancer. The PDS calculation involves only 16 genes, which supports its potential for clinical application.

Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer

Background

The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.

Results

We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.

Conclusions

Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.

A Combined Epithelial Mesenchymal Transformation and DNA Repair Gene Panel in Colorectal Cancer With Prognostic and Therapeutic Implication

Background

Epithelial mesenchymal transformation (EMT) and DNA repair status represent intrinsic features of colorectal cancer (CRC) and are associated with patient prognosis and treatment responsiveness. We sought to develop a combined EMT and DNA repair gene panel with potential application in patient classification and precise treatment.

Methods

We comprehensively evaluated the EMT and DNA repair patterns of 1,652 CRC patients from four datasets. Unsupervised clustering was used for classification. The clinical features, genetic mutation, tumor mutation load, and chemotherapy as well as immunotherapy sensitivity among different clusters were systematically compared. The least absolute shrinkage and selection operator regression method was used to develop the risk model.

Results

Three distinct CRC clusters were determined. Clustet1 was characterized by down-regulated DNA repair pathways but active epithelial markers and metabolism pathway and had intermediate prognosis. Clustet2 was characterized by down-regulated both epithelial markers and DNA repair pathways and had poor outcome. Clustet3 presented with activation of DNA repair pathway and epithelial markers had favorable prognosis. Clustet1 might benefit form chemotherapy and Clustet3 had a higher response rate to immunotherapy. An EMT and DNA repair risk model related to prognosis and treatment response was developed.

Conclusions

This work developed and validated a combined EMT and DNA repair gene panel for CRC classification, which may be an effective tool for survival prediction and treatment guidance in CRC patients.

Construction of immune-related gene pairs signature to predict the overall survival of osteosarcoma patients

The purpose of this study is to establish the prognosis of osteosarcoma patients based on the characteristics of immune-related gene pairs. We used the lasso Cox regression model to construct and verify the signature consisting of 14 immune-related gene pairs. This signature can accurately predict the overall survival of osteosarcoma patients and is an independent prognostic factor for osteosarcoma patients. For this we constructed a signature-based nomogram. The results of the nomogram show that our signature can bring clinical net benefits. We then assessed the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. The result of gene set enrichment analysis shows the strong relationship between signature and immune system. Finally, we evaluated the relationship between signature and immunotherapy efficiency using algorithms such as TIMI and SubMap to explore patients who might benefit from immunotherapy. In conclusion, our signature can predict the overall survival rate of osteosarcoma patients and provide potential guidance for exploring patients who may benefit from immunotherapy.

Emerging Role of Immunotherapy for Colorectal Cancer with Liver Metastasis

Colorectal cancer (CRC) is the third most common malignant tumor in the world and the second leading cause of cancer-related deaths, with the liver as the most common site of distant metastasis. The prognosis of CRC with liver metastasis is poor, and most patients cannot undergo surgery. In addition, conventional antitumor approaches such as chemotherapy, radiotherapy, targeted therapy, and surgery result in unsatisfactory outcomes. In recent years, immunotherapy has shown good prospects in the treatment of assorted tumors by enhancing the host's antitumor immune function, and it may become a new effective treatment for liver metastasis of CRC. However, challenges remain in applying immunotherapy to CRC with liver metastasis. This review examines how the microenvironment and immunosuppressive landscape of the liver favor tumor progression. It also highlights the latest research advances in immunotherapy for colorectal liver metastasis and identifies immunotherapy as a treatment regimen with a promising future in clinical applications.

Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma

N6-methyladenosine (m6A) modification can regulate a variety of biological processes. However, the implications of m6A modification in lung adenocarcinoma (LUAD) remain largely unknown. Here, we systematically evaluated the m6A modification features in more than 2400 LUAD samples by analyzing the multi-omics features of 23 m6A regulators. We depicted the genetic variation features of m6A regulators, and found mutations of FTO and YTHDF3 were linked to worse overall survival. Many m6A regulators were aberrantly expressed in tumors, among which FTO, IGF2BP3, YTHDF1 and RBM15 showed consistent alteration features across 11 independent cohorts. Besides, the regulator-pathway interaction network demonstrated that m6A modification was associated with various biological pathways, including immune-related pathways. The correlation between m6A regulators and tumor microenvironment was also assessed. We found that LRPPRC was negatively correlated with most tumor-infiltrating immune cells. On the other hand, we established a scoring tool named m6Sig, which was positively correlated with PD-L1 expression and could reflect both the tumor microenvironment characterization and prognosis of LUAD patients. Comparison of CNV between high and low m6Sig groups revealed differences on chromosome 7. Application of m6Sig on an anti-PD-L1 immunotherapy cohort confirmed that the high m6Sig group demonstrated therapeutic advantages and clinical benefits. Our study indicated that m6A modification is involved in many aspects of LUAD and contributes to tumor microenvironment formation. A better understanding of m6A modification will provide more insights into the molecular mechanisms of LUAD and facilitate developing more effective personalized treatment strategies. A web application was built along with this study (http://www.bioinfo-zs.com/luadexpress/).

Identification of biomarkers predicting the chemotherapeutic outcomes of capecitabine and oxaliplatin in patients with gastric cancer

The capecitabine and oxaliplatin (CapeOX) regimen is a commonly used adjuvant chemotherapeutic regimen for gastric cancer (GC). However, some patients exhibit a poor chemotherapy response due to genetic differences among individuals. Therefore, finding an effective sensitization strategy for CapeOX is important in the treatment of GC. The present study aimed to investigate the predictive biomarkers of the CapeOX chemotherapeutic outcomes for patients with GC. A total of 30 differentially expressed genes (DEGs) were identified using the gene expression profiles from The Cancer Genome Atlas capecitabine and oxaliplatin treatment GC cases and seven key DEGs [uroplakin-1b (UPK1B), fatty acid-binding protein, heart (FABP3), cystatin-M, caspase-5 (CASP5), corticosteroid 11-β-dehydrogenase isozyme 2, cytochrome P450 4X1 (CYP4X1) and epidermal growth factor receptor kinase substrate 8-like protein 3] were associated with survival. Gene validation was performed in clinical samples divided into recurrence and nonrecurrence groups. Patients with high or low expression of UPK1B, FABP3, CASP5 and CYP4X1 had markedly different overall survival rates. A model was established and the area under the curve of the receiver operating characteristic reached 0.875 (0.793–0.957), indicating that the model had good sensitivity and specificity.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Oral Squamous Cell Carcinoma

Background

Alternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC).

Methods

Seven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape.

Results

The final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events.

Conclusions

We developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.

Prognostic Impact of Total Plasma Cell-free DNA Concentration in Androgen Receptor Pathway Inhibitor-treated Metastatic Castration-resistant Prostate Cancer

Total plasma cell-free DNA (cfDNA) levels were recently shown to be prognostic in two large phase III trials of taxane chemotherapy in metastatic castration-resistant prostate cancer (mCRPC). However, whether cfDNA concentration is predictive of treatment outcomes with androgen receptor pathway inhibitors (ARPIs) is unknown. We quantified plasma cfDNA levels at baseline (n = 74) and 4 weeks on treatment (n = 56) in a prospective cohort of mCRPC patients treated with the ARPIs abiraterone acetate or enzalutamide. Elevated total cfDNA concentration (log₁₀) at both baseline (hazard ratio [HR] 5.5, p < 0.001) and week 4 (HR 7.5, p < 0.001) was a significant negative prognostic factor for overall survival (OS), a finding maintained after adjustment for plasma circulating tumour DNA fraction. Unexpectedly, a rise in cfDNA concentration from baseline to week 4 was also associated with significantly improved OS (HR 0.14, p = 0.003). Conversely, patients with ≥29.8% decrease in cfDNA from baseline (optimal cut-point) had significantly shorter median OS than the rest of the cohort (10.5 vs 25.7 mo, p = 0.03). Collectively, our findings point to the potential prognostic utility of quantifying cfDNA in mCRPC and in particular suggest that dynamic changes in total cfDNA levels may be a novel early predictive biomarker for therapeutic outcome in ARPI-treated patients. PATIENT SUMMARY: We measured the levels of total cell-free DNA (cfDNA) in the plasma of patients with metastatic prostate cancer prior to and 4 weeks after starting new hormonal drugs. We found that patients with higher levels of cfDNA or a higher proportion of tumour-derived DNA at baseline had worse outcomes on hormonal therapies. Similarly, higher levels of cfDNA at 4 weeks into therapy were also associated with worse outcomes. However, a rise in total cfDNA levels at 4 weeks compared with baseline was linked with better outcomes. Measuring changes in cfDNA concentration may be a useful and technically straightforward early way to predict how patients will respond to treatment in metastatic prostate cancer.

Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer

Immune-checkpoint blockades (ICBs) have been routinely implemented to treat metastatic urothelial cancer (mUC), whereas robust biomarkers are urgently warranted. Herein, we explored latent promising biomarkers based on 348 pretreatment mUC samples from IMvigor210. Methods: The genome, transcriptome, immunome, and metabolome were systemically analyzed using the external TCGA dataset for validation. Kaplan-Meier and ROC curve analyses were performed to estimate the predictive capacity of M1-macrophage infiltration. Chi-square/Spearman/Mann Whitney U test are used to determine its correlation to genetic, biochemical, and clinicopathological parameters. Results: M1 frequency is a robust biomarker for predicting the prognosis and response to ICBs, which is non-inferior to tumor mutation burden (TMB) or tumor neoantigen burden (TNB), and exceeds CD8 T cells, T cell inflamed gene expression profile (GEP), and PD-L1 expression. Moreover, M1 infiltration is associated with immune phenotypes (AUC = 0.785) and is negatively correlated with immune exclusion. Additionally, transcriptomic analysis showed immune activation in the high-M1 subgroup, whereas it showed steroid and drug metabolism reprograming in the M1-deficient subset, which characterized the limited sensitivity to ICB therapy. Notably, investigation of the corresponding intrinsic genomic profiles highlighted the significance of TP53 and FGFR alterations. Conclusions: M1 infiltration is a robust biomarker for immunotherapeutic response and immunophenotype determination in an mUC setting. Innate immunity activation involving macrophage polarization remodeling and anti-FGFR mutations may be promising strategies for synergy with anti-PD-L1 treatments and may help prolong the clinical survival of patients with mUC.

A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.