Long non-coding RNAs (lncRNAs) gradually show critical regulatory roles in many malignancies. However, the lncRNAs implicated in colon cancer recurrence are largely unknown. In this study, we searched the lncRNAs associated with metastasis and recurrence of colon cancer using GEO datasets. We focused on a novel lncRNA long non-coding RNA associated with poor prognosis of colon cancer (LNAPPCC), which is highly expressed in colon cancer. Increased expression of LNAPPCC is positively associated with metastasis, recurrence, and poor survival of colon cancer patients. LNAPPCC promotes colon cancer cell proliferation, migration, and in vivo xenograft growth and liver metastasis. Mechanistic investigations revealed that LNAPPCC binds EZH2, represses the binding of EZH2 to PCDH7 promoter, downregulates histone H3K27me3 level in PCDH7 promoter, and activates PCDH7 expression. Intriguingly, we also found that PCDH7 activates ERK/c-FOS signaling, increases the binding of c-FOS to LNAPPCC promoter, and activates LNAPPCC expression. Therefore, LNAPPCC and PCDH7 form a positive regulatory loop via EZH2 and ERK/c-FOS. The positive correlations between the expression of LNAPPCC, PCDH7, phosphorylated ERK, and phosphorylated c-FOS are detected in colon cancer tissues. Furthermore, depletion of PCDH7 or the adding of ERK inhibitor abolished the oncogenic roles of LNAPPCC in colon cancer. In summary, this study identified a novel lncRNA LNAPPCC that is highly expressed in colon cancer and associated with poor prognosis of colon cancer patients. LNAPPCC exerts oncogenic roles in colon cancer via forming a positive feedback loop with PCDH7. Targeting LNAPPCC/EZH2/ PCDH7/ERK/c-FOS signaling axis represents a potential therapeutic strategy for colon cancer.

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This paper aims to evaluate the impact of customer allocation on the facility location in the multi-objective location problem for sustainable logistics. After a new practical multi-objective location model considering vehicle carbon emissions is introduced, the NSGA-II and SEAMO2 algorithms are employed to solve the model. Within the framework of each algorithm, three different allocation rules derived from the optimization of customer allocation based on distance, cost, and emissions are separately applied to perform the customer-to-facility assignment so as to evaluate their impacts. The results of extensive computational experiments show that the allocation rules have nearly no influence on the solution quality, and the allocation rule based on the distance has an absolute advantage of computation time. These findings will greatly help to simplify the location-allocation analysis in the multi-objective location problems.