Here, we aimed to identify an immunohistochemical (IHC)‐based classifier as a prognostic factor in patients with esophageal squamous cell carcinoma (ESCC). A cohort of 235 patients with ESCC undergoing radical esophagectomy (with complete clinical and pathological information) were enrolled in the study. Using the least absolute shrinkage and selection operator (LASSO) regression model, we extracted six IHC features associated with progression‐free survival (PFS) and then built a classifier in the discovery cohort (n = 141). The prognostic value of this classifier was further confirmed in the validation cohort (n = 94). Additionally, we developed a nomogram integrating the IHC‐based classifier to predict the PFS. We used the IHC‐based classifier to stratify patients into high‐ and low‐risk groups. In the discovery cohort, 5‐year PFS was 22.4% (95% CI: 0.14–0.36) for the high‐risk group and 43.3% (95% CI: 0.32–0.58) for the low‐risk group (P = 0.00064), and in the validation cohort, 5‐year PFS was 20.58% (95% CI: 0.12–0.36) for the high‐risk group and 36.43% (95% CI: 0.22–0.60) for the low‐risk group (P = 0.0082). Multivariable analysis demonstrated that the IHC‐based classifier was an independent prognostic factor for predicting PFS of patients with ESCC. We further developed a nomogram integrating the IHC‐based classifier and clinicopathological risk factors (gender, American Joint Committee on Cancer staging, and vascular invasion status) to predict the 3‐ and 5‐year PFS. The performance of the nomogram was evaluated and proved to be clinically useful. Our 6‐IHC marker‐based classifier is a reliable prognostic tool to facilitate the individual management of patients with ESCC after radical esophagectomy.

Background: Increasing evidence showed that the clinical significance of the interaction between hypoxia and immune status in tumor microenvironment. However, reliable biomarkers based on the hypoxia and immune status in triple-negative breast cancer (TNBC) have not been well established. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC. Methods: Hypoxia-related genes (HRGs) and Immune-related genes (IRGs) were identified using the weighted gene co-expression network analysis (WGCNA) method and the single-sample gene set enrichment analysis (ssGSEA Z-score) with the transcriptomic profiles from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Then, prognostic hypoxia and immune based genes were identified in TNBC patients from the METABRIC (N = 221), The Cancer Genome Atlas (TCGA) (N = 142), and GSE58812 (N = 107) using univariate cox regression model. A robust hypoxia-immune based gene signature for prognosis was constructed using the least absolute shrinkage and selection operator (LASSO) method. Based on the crosscohort prognostic hypoxia-immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia-immune status were identified. The prognosis value, hypoxia and immune status, and therapeutic response in different risk groups were analyzed. Furthermore, a nomogram was constructed to predict the prognosis for individual patients, and an independent cohort from the gene expression omnibus (GEO) database was used for external validation. Results: Six cross-cohort prognostic hypoxia-immune related genes were identified to establish the comprehensive index of hypoxia and immune. Then, patients were clustered into high-and low-risk groups based on the hypoxia-immune status. Patients in the high-risk group showed poorer prognoses to their lowrisk counterparts, and the nomogram we constructed yielded favorable performance to predict survival and risk stratification. Besides, the high-risk group had a higher expression of hypoxia-related genes and correlated with hypoxia status in tumor microenvironment. The high-risk group had lower fractions of activated immune cells, and exhibited lower expression of immune checkpoint markers. Furthermore, the ratio of complete response (CR) was greatly declined, and the ratio of breast cancer related events were significantly elevated in the highrisk group. Conclusion: The hypoxia-immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.

Angioimmunoblastic T-cell lymphoma (AITL) is recognized as a distinct clinicopathological subtype of peripheral T-cell lymphomas. Its clinical features include generalized lymphadenopathy, constitutional symptoms, and autoimmune-related findings, such as hemolytic anemia. Pathologically, AITL is characterized by a polymorphous infiltrate in lymph nodes with prominent proliferation of high endothelial venules and follicular dendritic cells. We present an 80-year-old Chinese man with generalized lymphadenopathy and pulmonary infection, diagnosed as AITL based on the distinctive pathological findings and T-cell receptor gamma (TCR-γ) gene rearrangement analysis of lymph nodes. Importantly, the patient suffered from a coexisting plasma cell myeloma, as judged by monoclonal immunoglobulin in the serum, immature plasma cells, and rearrangement of the immunoglobulin heavy-chain (IgH) gene in the bone marrow. The patient received two courses of the chemotherapy but died of pneumonia 6 months after diagnosis. AITL can be accompanied by polyclonal or clonal proliferation of B lymphocytes; however, AITL are rarely associated with plasma cell proliferation. In fact, 14 AITL cases with plasma cell proliferation have been reported in the literature, but none of them manifested the infiltration of monoclonal immature plasma cells in the bone marrow. To the best of our knowledge, this is the first report of newly diagnosed, concurrent AITL and plasma cell myeloma, providing the evidence for the interplay between malignant T cells and plasma cell proliferation. A review of the literature has also supported a relationship between AITL and plasma cell proliferation. Awareness of this relationship is important for correct diagnosis and appropriate treatment of AITL.

Objectives To screen and verify differential genes affecting the prognosis of breast cancer. Methods Breast cancer gene expression datasets were downloaded from the GEO database, and original data were analyzed in R. The TIMER database was used to analyze the relationship between ANLN and UBE2T and immune cell infiltration. Results Ten hub-key genes were identified, and survival analysis showed that UBE2T and ANLN were upregulated in breast cancer and their upregulation was associated with a poor prognosis. ANLN and UBE2T upregulation was associated with the prevalence of Th1 and Th2 cells, shifting the Th1/Th2 balance to Th2 in Basal and Luminal-B breast cancers, which indicates a poor prognosis (P < 0.05). Conclusion ANLN and UBE2T are potential biomarkers for predicting the prognosis of breast cancer.