Tumor-infiltrating immune cells (TIICs) play essential roles in cancer development and progression. However, the association of TIICs with prognosis in colorectal cancer (CRC) patients remains elusive. Infiltration of TIICs was assessed using ssGSEA and CIBERSORT tools. The association of TIICs with prognosis was analyzed in 1,802 CRC data downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/) databases. Three populations of TIICs, including CD66b+ tumor-associated neutrophils (TANs), FoxP3+ Tregs, and CD163+ tumor-associated macrophages (TAMs) were selected for immunohistochemistry (IHC) validation analysis in 1,008 CRC biopsies, and their influence on clinical features and prognosis of CRC patients was analyzed. Prognostic models were constructed based on the training cohort (359 patients). The models were further tested and verified in testing (249 patients) and validation cohorts (400 patients). Based on ssGSEA and CIBERSORT analysis, the correlation between TIICs and CRC prognosis was inconsistent in different datasets. Moreover, the results with disease-free survival (DFS) and overall survival (OS) data in the same dataset also differed. The high abundance of TIICs found by ssGSEA or CIBERSORT tools can be used for prognostic evaluation effectively. IHC results showed that TANs, Tregs, TAMs were significantly correlated with prognosis in CRC patients and were independent prognostic factors (PDFS ≤ 0.001; POS ≤ 0.023). The prognostic predictive models were constructed based on the numbers of TANs, Tregs, TAMs (C-indexDFS&OS = 0.86; AICDFS = 448.43; AICOS = 184.30) and they were more reliable than traditional indicators for evaluating prognosis in CRC patients. Besides, TIICs may affect the response to chemotherapy. In conclusion, TIICs were correlated with clinical features and prognosis in patients with CRC and thus can be used as markers.

Purpose We previously found that human cytomegalovirus (HCMV) infection is associated with gastric cancer (GC) development. UL111A plays a role during HCMV productive or latent infection. However, UL111A expression profiles in GC tissues and their relationship with this disease are unknown. Methods PCR and nested RT-PCR were performed to verify UL111A expression in 71 GC tissues and its transcripts in 16 UL111A-positive GC samples. UL111A expression levels in GC patients were evaluated by immunohistochemistry on a tissue microarray for 620 GC patients. The correlations among UL111A expression levels, clinicopathological characteristics, and prognosis were analyzed. Further, the effects of overexpression of latency-associated viral interleukin-10 (LAcmvIL-10) and cmvIL-10 on GC cell proliferation, colony formation, migration, and invasion were assessed. Results The UL111A detection rate in GC tissues was 32.4% (23/71) and that of its mRNA expression was 68.75% (11/16). High expression of UL111A was also related to better overall and disease-free survival in GC patients. GC patients with TNM II/III stage expressing higher UL111A levels might benefit from adjuvant chemotherapy (ACT) after surgery. Moreover, high UL111A expression was also associated with increased CD4+ , CD8+ T-lymphocyte and Foxp3+ T-cell infiltration. In vitro assays further demonstrated that LAcmvIL-10 and cmvIL-10 overexpression inhibits GC cell line proliferation, colony formation, migration, and invasion. Conclusions High UL111A expression changes the number of infiltrating T cells and is associated with favorable survival. Therefore, UL111A could be used as an independent prognostic biomarker and might be a potential therapeutic target for GC.

Objective: Pathogen infection plays a role in the development and progression of systemic lupus erythematosus (SLE). Previous studies showed that peripheral blood mononuclear cells (PBMCs) harbor many viral communities. However, little is known about the viral components and the expression profiles of SLE-associated virome. We aimed to identify viral taxonomic markers of SLE that might be used in the detection of disease or in predicting its outcome.Methods: Non-human sequence data from high-throughput transcriptome sequencing of PBMC samples from 10 SLE patients and 10 healthy individuals were used for taxonomic alignment against an integrated virome reference genome database. Based on abundance profiles of SLE-associated virome species, genera, or host, Random Forests model was used to identify the viruses associated with SLE diagnostic markers. Spearman's correlation and functional clustering was used to analyze the interaction of candidate virome dysbiosis and SLE-associated differentially expressed genes.Results: A total of 419 viruses (38 human associated viruses, 350 phage, and 31 other viruses) was detected and the diversity of the PBMC virome was significantly increased in patients with SLE compared to the healthy controls (HCs). Viral taxa discriminated the cases from the controls, with an area under the receiver operating characteristic curve of 0.883, 0.695, and 0.540 for species, genus, and host, respectively. Clinical subgroup analysis showed that candidate PBMC viral markers were associated with stable-and active-stage SLE. Functional analyses showed that virome dysbiosis was mainly relevant to cellular and metabolic processes. Conclusion:We identified virome signatures associated with SLE, which might help develop tools to identify SLE patients or predict the disease stage.

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