Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

Background The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. However, the heterogeneous TIME and the mechanisms by which TIME affects immunotherapy have not been elucidated in hepatocellular carcinoma (HCC). Methods A total of 2195 eligible HCC patients from TCGA and GEO database were collected. We comprehensively explored the different heterogeneous TIME phenotypes and its clinical significance. The potential immune escape mechanisms and what genomic alterations may drive the formation of different phenotypes were further investigated. Results We identified three phenotypes in HCC: TIME-1, the “immune-deficiency” phenotype, with immune cell depletion and proliferation; TIME-2, the “immune-suppressed” phenotype, with enrichment of immunosuppressive cells; TIME-3, the “immune-activated phenotype”, with abundant leukocytes infiltration and immune activation. The prognosis and sensitivity to both sorafenib and immunotherapy differed among the three phenotypes. We also underlined the potential immune escape mechanisms: lack of leukocytes and defective tumor antigen presentation capacity in TIME-1, increased immunosuppressive cells in TIME-2, and rich in immunoinhibitory molecules in TIME-3. The different phenotypes also demonstrated specific genomic events: TIME-1 characterized by TP53, CDKN2A, CTNNB1, AXIN1 and FOXD4 alterations; TIME-2 characterized by significant alteration patterns in the PI3K pathway; TIME-3 characterized by ARID1A mutation. Besides, the TIME index (TI) was proposed to quantify TIME infiltration pattern, and it was a superior prognostic and immunotherapy predictor. A pipeline was developed to classify single patient into one of these three subtypes and calculated the TI. Conclusions We identified three TIME phenotypes with different clinical outcomes, immune escape mechanisms and genomic alterations in HCC, which could present strategies for improving the efficacy of immunotherapy. TI as a novel prognostic and immunotherapeutic signature that could guide personalized immunotherapy and clinical management of HCC.

Colorectal cancer (CRC) is one of the most prevalent gastrointestinal malignancies with adverse prognosis. Currently, it has become the fourth most deadly tumour globally and accounts for approximately 10% of annual cancer-associated deaths. 1 Surgery or endoscopic treatment is the cornerstone of curative therapy for patients with CRC. Chemoradiotherapy such as fluoropyrimidine combined with

Genomic alterations constitute crucial elements of colorectal cancer (CRC). However, a comprehensive understanding of CRC genomic alterations from a global perspective is lacking. In this study, a total of 2,778 patients in 15 public datasets were enrolled. Tissues and clinical information of 30 patients were also collected. We successfully identified two distinct mutation signature clusters (MSC) featured by massive mutations and dominant somatic copy number alterations (SCNA), respectively. MSC-1 was associated with defective DNA mismatch repair, exhibiting more frequent mutations such as ATM, BRAF, and SMAD4. The mutational co-occurrences of BRAF-HMCN and DNAH17-MDN1 as well as the methylation silence event of MLH-1 were only found in MSC-1. MSC-2 was linked to the carcinogenic process of age and tobacco chewing habit, exhibiting dominant SCNA such as MYC (8q24.21) and PTEN (10q23.31) deletion as well as CCND3 (6p21.1) and ERBB2 (17q12) amplification. MSC-1 displayed higher immunogenicity and immune infiltration. MSC-2 had better prognosis and significant stromal activation. Based on the two subtypes, we identified and validated the expression relationship of FAM83A and IDO1 as a robust biomarker for prognosis and distant metastasis of CRC in 15 independent cohorts and qRT-PCR data from 30 samples. These results advance precise treatment and clinical management in CRC.

Immune checkpoint inhibitors (ICIs) have completely changed the approach pertaining to tumor diagnostics and treatment. Similarly, immunotherapy has also provided much needed data about mutation, expression and prognosis, affording an unprecedented opportunity for discovering candidate drug targets and screening for immunotherapy-relevant biomarkers. Although existing web tools enable biologists to analyze the expression, mutation and prognostic data of tumors, they are currently unable to facilitate data mining and mechanism analyses specifically related to immunotherapy. Thus, we effectively developed our own web-based tool, called Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer (CAMOIP), in which we are able to successfully screen various prognostic markers and analyze the mechanisms involved in biomarker expression and function, as well as immunotherapy. The analyses include information relevant to survival analysis, expression analysis, mutational landscape analysis, immune infiltration analysis, immunogenicity analysis and pathway enrichment analysis. This comprehensive analysis of biomarkers for immunotherapy can be carried out by a click of CAMOIP, and the software should greatly encourage the further development of immunotherapy. CAMOIP provides invaluable evidence that bridges the information between the data of cancer genomics based on immunotherapy, providing comprehensive information to users and assisting in making the value of current ICI-treated data available to all users. CAMOIP is available at https://www.camoip.net.

BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

The immune microenvironment has profound impacts on the initiation and progression of colorectal cancer (CRC). Therefore, the goal of this article is to identify two robust immune subtypes in CRC, further provide novel insights for the underlying mechanisms and clinical management. In this study, two CRC immune subtypes were identified using the consensus clustering of immune-related gene expression profiles in the meta-GEO dataset (n = 1,198), and their reproducibility was further verified in the TCGA-CRC dataset (n = 638). Subsequently, we characterized the immune escape mechanisms, gene alterations, and clinical features of two immune subtypes. Cluster 1 (C1) was defined as the “immune cold subtype” with immune cell depletion and deficiency, while cluster 2 (C2) was designed as the “immune hot subtype”, with abundant immune cell infiltration and matrix activation. We also underlined the potential immune escape mechanisms: lack of MHC molecules and defective tumor antigen presentation capacity in C1, increased immunosuppressive molecules in C2. The prognosis and sensitivity to 5-FU, Cisplatin and immunotherapy differed between two subtypes. According to the two immune subtypes, we developed a prognosis associated risk score (PARS) with the accurate performance for predicting the prognosis. Additionally, two nomograms for overall survival (OS) and disease-free survival (DFS) were further constructed to facilitate clinical management. Overall, our research provides new references and insights for understanding and refining the CRC.

BackgroundLung adenocarcinoma (LUAD) is a fatal malignancy in the world. Growing evidence demonstrated that autophagy-related genes regulated the immune cell infiltration and correlated with the prognosis of LUAD. However, the autophagy-based signature that can predict the prognosis and the efficiency of checkpoint immunotherapy in LUAD patients is yet to be discovered.MethodsWe used conventional autophagy-related genes to screen candidates for signature construction in TCGA cohort and 9 GEO datasets (tumor samples, n=2181; normal samples, n=419). An autophagy-based signature was constructed, its correlation with the prognosis and the immune infiltration of LUAD patients was explored. The prognostic value of the autophagy-based signature was validated in an independent cohort with 70 LUAD patients. Single-cell sequencing data was used to further characterize the various immunological patterns in tumors with different signature levels. Moreover, the predictive value of autophagy-based signature in PD-1 immunotherapy was explored in the IMvigor210 dataset. At last, the protective role of DRAM1 in LUAD was validated by in vitro experiments.ResultsAfter screening autophagy-related gene candidates, a signature composed by CCR2, ITGB1, and DRAM1 was established with the ATscore in each sample. Further analyses showed that the ATscore was significantly associated with immune cell infiltration and low ATscore indicated poor prognosis. Meanwhile, the prognostic value of ATscore was validated in our independent LUAD cohort. GSEA analyses and single-cell sequencing analyses revealed that ATscore was associated with the immunological status of LUAD tumors, and ATscore could predict the efficacy of PD-1 immunotherapy. Moreover, in vitro experiments demonstrated that the inhibition of DRAM1 suppressed the proliferation and migration capacity of LUAD cells.ConclusionOur study identified a new autophagy-based signature that can predict the prognosis of LUAD patients, and this ATscore has potential applicative value in the checkpoint therapy efficiency prediction.